U.S. patent application number 17/018629 was filed with the patent office on 2021-08-05 for multicellular metabolic models and methods.
The applicant listed for this patent is Genomatica, Inc.. Invention is credited to Imandokht Famili, Christophe H. Schilling.
Application Number | 20210241846 17/018629 |
Document ID | / |
Family ID | 1000005523301 |
Filed Date | 2021-08-05 |
United States Patent
Application |
20210241846 |
Kind Code |
A1 |
Famili; Imandokht ; et
al. |
August 5, 2021 |
MULTICELLULAR METABOLIC MODELS AND METHODS
Abstract
The invention provides a computer readable medium or media,
having: (a) a first data structure relating a plurality of
reactants to a plurality of reactions from a first cell, each of
said reactions comprising a reactant identified as a substrate of
the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating said substrate and said
product; (b) a second data structure relating a plurality of
reactants to a plurality of reactions from a second cell, each of
said reactions comprising a reactant identified as a substrate of
the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating said substrate and said
product; (c) a third data structure relating a plurality of
intra-system reactants to a plurality of intra-system reactions
between said first and second cells, each of said intra-system
reactions comprising a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product; (d) a constraint set for said plurality of reactions for
said first, second and third data structures, and (e) commands for
determining at least one flux distribution that minimizes or
maximizes an objective function when said constraint set is applied
to said first and second data structures, wherein said at least one
flux distribution is predictive of a physiological function of said
first and second cells. The first, second and third data structures
also can include a plurality of data structures. Additionally
provided is a method for predicting a physiological function of a
multicellular organism. The method includes: (a) providing a first
data structure relating a plurality of reactants to a plurality of
reactions from a first cell, each of said reactions comprising a
reactant identified as a substrate of the reaction, a reactant
identified as a product of the reaction and a stoichiometric
coefficient relating said substrate and said product; (b) providing
a second data structure relating a plurality of reactants to a
plurality of reactions from a second cell, each of said reactions
comprising a reactant identified as a substrate of the reaction, a
reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product; (c) providing a third data structure relating a plurality
of intra-system reactants to a plurality of intra-system reactions
between said first and second cells, each of said intra-system
reactions comprising a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product; (d) providing a constraint set for said plurality of
reactions for said first, second and third data structures; (e)
providing an objective function, and (f) determining at least one
flux distribution that minimizes or maximizes an objective function
when said constraint set is applied to said first and second data
structures, wherein said at least one flux distribution is
predictive of a physiological function of said first and second
cells.
Inventors: |
Famili; Imandokht; (San
Diego, CA) ; Schilling; Christophe H.; (San Diego,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Genomatica, Inc. |
San Diego |
CA |
US |
|
|
Family ID: |
1000005523301 |
Appl. No.: |
17/018629 |
Filed: |
September 11, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14572615 |
Dec 16, 2014 |
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17018629 |
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11188136 |
Jul 21, 2005 |
8949032 |
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14572615 |
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10402854 |
Mar 27, 2003 |
8229673 |
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11188136 |
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60368588 |
Mar 29, 2002 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 33/5008 20130101;
G01N 2500/10 20130101; G16C 20/30 20190201; G16B 50/00 20190201;
G16B 5/00 20190201; G16B 20/00 20190201 |
International
Class: |
G16B 5/00 20060101
G16B005/00; G16B 50/00 20060101 G16B050/00; G16C 20/30 20060101
G16C020/30; G01N 33/50 20060101 G01N033/50 |
Claims
1-60. (canceled)
61. A computer-implemented method for predicting a physiological
function of single-celled organisms, comprising: (a) providing on a
computer a first data structure comprising a first stoichiometric
matrix having rows and columns of elements that correspond to
stoichiometric coefficients of a plurality of first reactions from
a first single-celled organism, each of said reactions comprising a
reactant identified as a substrate of the reaction and a reactant
identified as a product of the reaction, stoichiometric
coefficients of the first stoichiometric matrix relating said
substrate and said product, wherein at least one reactant in said
plurality of reactants or at least one reaction in said plurality
of reactions is annotated with an assignment to a subsystem or
compartment; (b) providing on the computer a second data structure
comprising a second stoichiometric matrix having rows and columns
of elements that correspond to stoichiometric coefficients of a
plurality of second reactions from a second single-celled organism,
each of said reactions comprising a reactant identified as a
substrate of the reaction and a reactant identified as a product of
the reaction, stoichiometric coefficients of the second
stoichiometric matrix relating said substrate and said product,
wherein at least one reactant in said plurality of reactants or at
least one reaction in said plurality of reactions is annotated with
an assignment to a subsystem or compartment; (c) providing on the
computer a third data structure comprising a third stoichiometric
matrix or elements in the first or second stoichiometric matrices
having rows and columns of elements that correspond to
stoichiometric coefficients of a plurality of intra-system
reactions between said first and second single-celled organisms and
an intra-cellular system of said first or second single-celled
organisms, each of said intra-system reactions comprising a
reactant identified as a substrate of the reaction located in one
of the first or second single-celled organisms or in the
intra-cellular system and a reactant identified as a product of the
reaction located in another of the first and second single-celled
organisms or in the intra-cellular system, stoichiometric
coefficients of the third stoichiometric matrix relating said
substrate and said product; (d) providing on the computer a
constraint set for said plurality of reactions for said first,
second and third data structures, the constraint set specifying an
upper or lower boundary of flux through each of the reactions
described in the first, second, and third stoichiometric matrices;
(e) defining on the computer an objective function to be a linear
combination of fluxes through the reactions described in the first,
second, and third stoichiometric matrices that optimizes cell
growth, reproduction, apoptosis, energy production, production of a
hormone or extracellular component, a mechanical property, or
maintenance of biomass composition and growth rate; (f) determining
on the computer at least one flux distribution for said plurality
of first, second and intra-system reactions across said first
single-celled organism, said second single-celled organism, and
said intra-cellular system by (i) identifying a plurality of flux
vectors that each satisfy the stoichiometric matrix and satisfy the
constraint set and (ii) identifying at least one linear combination
of the flux vectors that minimizes or maximizes the objective
function; and (g) providing output to a user of said at least one
flux distribution determined in step (f), wherein said at least one
flux distribution is predictive of a physiological function of said
first and second single-celled organisms.
62. The method of claim 61, wherein said first data structure
comprises a first reaction network.
63. The method of claim 61, wherein said second data structure
comprises a second reaction network.
64. The method of claim 61, wherein said first or second data
structures comprise a plurality of reaction networks.
65. The method of claim 61, further comprising providing on the
computer one or more fourth data structures comprising one or more
fourth stoichiometric matrices and one or more fourth constraint
sets, each fourth data structure relating a plurality of reactants
to a plurality of one or more third reactions from one or more
third single-celled organisms, each of said reactions comprising a
reactant identified as a substrate of the reaction, a reactant
identified as a product of the reaction and a stoichiometric
coefficient relating said substrate and said product.
66. The method of claim 65, wherein said one or more fourth data
structures comprises a plurality of data structures.
67. The method of claim 66, wherein said plurality of data
structures comprise a data structure for a plurality of different
single-celled organisms.
68. The method of 65, wherein said one or more third single-celled
organism comprises at least 4 single-celled organisms, 5
single-celled organisms, 6 single-celled organisms, 7 single-celled
organisms, 8 single-celled organisms, 9 single-celled organisms, 10
single-celled organisms, 100 single-celled organisms, 1000
single-celled organisms, 5000 single-celled organisms, 10,000
single-celled organisms or more.
69. The method of claim 61, wherein said first and second
single-celled organisms comprise eukaryotic cells.
70. The method of claim 61, wherein said first and second
single-celled organisms comprise prokaryotic cells.
71. The method of claim 61, further comprising accessing with the
computer a gene database having information characterizing an
associated gene.
72. The method of claim 61, wherein at least one of said reactions
is a regulated reaction.
73. The method of claim 72, wherein said constraint set includes a
variable constraint for said regulated reaction.
74. The method of claim 61, wherein said intra-system reactions
comprise a reactant or reactions selected from the group consisting
of a bicarbonate buffer system, an ammonia buffer system, a
hormone, a signaling molecule, a vitamin, a mineral or a
combination thereof.
75. The method of claim 61, wherein a plurality of reactions is
annotated to indicate a plurality of associated genes and wherein
said gene database comprises information characterizing said
plurality of associated genes.
Description
[0001] This application is a continuation of U.S. patent
application Ser. No. 14/572,615, filed Dec. 16, 2014, which is a
continuation of U.S. patent application Ser. No. 11/188,136, filed
Jul. 21, 2005, now U.S. Pat. No. 8,949,032, which is a
continuation-in-part of U.S. patent application Ser. No.
10/402,854, filed Mar. 27, 2003, now U.S. Pat. No. 8,229,673, which
claims benefit of the filing date of U.S. Provisional Application
No. 601368,588, filed Mar. 29, 2002, the entire contents of each of
which is incorporated herein by reference.
BACKGROUND OF THE INVENTION
[0002] This invention relates generally to analysis of the activity
of chemical reaction networks and, more specifically, to
computational methods for simulating and predicting the activity of
multiple interacting reaction networks.
[0003] Therapeutic agents, including drugs and gene-based agents,
are being rapidly developed by the pharmaceutical industry with the
goal of preventing or treating human disease. Dietary supplements,
including herbal products, vitamins and amino acids, are also being
developed and marketed by the nutraceutical industry. Because of
the complexity of the biochemical reaction networks in and between
human cells, even relatively minor perturbations caused by a
therapeutic agent or a dietary component in the abundance or
activity of a particular target, such as a metabolite, gene or
protein, can affect hundreds or biochemical reactions. These
perturbations can lead to desirable therapeutic effects, such as
cell stasis or cell death in the case of cancer cells or other
pathologically hyperproliferative cells. However, these
perturbations can also lead to undesirable side effects, such as
production of toxic byproducts, if the systemic effects of the
perturbations are not taken into account.
[0004] Current approaches to drug and nutraceutical development do
not take into account the effect of a perturbation in a molecular
target on systemic cellular behavior. In order to design effective
methods of repairing, engineering or disabling cellular activities.
it is essential to understand human cellular behavior from an
integrated perspective.
[0005] Cellular metabolism, which is an example of a process
involving a highly integrated network of biochemical reactions, is
fundamental to all normal cellular or physiological processes,
including homeostatis, proliferation, differentiation, programmed
cell death (apoptosis) and motility. Alterations in cellular
metabolism characterize a vast number of human diseases. For
example, tissue injury is often characterized by increased
catabolism of glucose, fatty acids and amino acids, which, if
persistent, can lead to organ dysfunction. Conditions of low oxygen
supply (hypoxia) and nutrient supply, such as occur in solid
tumors, result in a myriad of adaptive metabolic changes including
activation of glycolysis and neovascularization. Metabolic
dysfunctions also contribute to neurodegenerative diseases,
cardiovascular disease, neuromuscular diseases, obesity and
diabetes. Currently, despite the importance of cellular metabolism
to normal and pathological processes, a detailed systemic
understanding of cellular metabolism in human cells is currently
lacking.
[0006] Thus, there exists a need for models that describe
interacting reaction networks within and between cells, including
core metabolic reaction networks and metabolic reaction networks in
specialized cell types, which can be used to simulate different
aspects of multicellular behavior under physiological, pathological
and therapeutic conditions. The present invention satisfies this
need, and provides related advantages as well.
SUMMARY OF THE INVENTION
[0007] The invention provides a computer readable medium or media,
having: (a) a first data structure relating a plurality of
reactants to a plurality of reactions from a first cell, each of
said reactions comprising a reactant identified as a substrate of
the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating said substrate and said
product; (b) a second data structure relating a plurality of
reactants to a plurality of reactions from a second cell, each of
said reactions comprising a reactant identified as a substrate of
the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating said substrate and said
product; (c) a third data structure relating a plurality of
intra-system reactants to a plurality of intra-system reactions
between said first and second cells, each of said intra-system
reactions comprising a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product; (d) a constraint set for said plurality of reactions for
said first, second and third data structures, and (e) commands for
determining at least one flux distribution that minimizes or
maximizes an objective function when said constraint set is applied
to said first and second data structures, wherein said at least one
flux distribution is predictive of a physiological function of said
first and second cells. The first, second and third data structures
also can include a plurality of data structures. Additionally
provided is a method for predicting a physiological function of a
multicellular organism. The method includes: (a) providing a first
data structure relating a plurality of reactants to a plurality of
reactions from a first cell, each of said reactions comprising a
reactant identified as a substrate of the reaction, a reactant
identified as a product of the reaction and a stoichiometric
coefficient relating said substrate and said product; (b) providing
a second data structure relating a plurality of reactants to a
plurality of reactions from a second cell, each of said reactions
comprising a reactant identified as a substrate of the reaction, a
reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product: (c) providing a third data structure relating a plurality
of intra-system reactants to a plurality of intra-system reactions
between said first and second cells, each of said intra-system
reactions comprising a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product; (d) providing a constraint set for said plurality of
reactions for said first, second and third data structures: (c)
providing an objective function, and (f) determining at least one
flux distribution that minimizes or maximizes an objective function
when said constraint set is applied to said first and second data
structures, wherein said at least one flux distribution is
predictive of a physiological function of said first and second
cells.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] FIG. 1 shows a schematic representation of a hypothetical
metabolic network.
[0009] FIG. 2 shows mass balance constraints and flux constraints
(reversibility constraints) that can be placed on the hypothetical
metabolic network shown in FIG. 1.
[0010] FIG. 3 shows the stoichiometric matrix (S) for the
hypothetical metabolic network shown in FIG. 1.
[0011] FIG. 4 shows, in Panel A, an exemplary biochemical reaction
network and in Panel B, an exemplary regulatory control structure
for the reaction network in panel A.
[0012] FIGS. 5-1 through 5-9 show a metabolic network of central
human metabolism.
[0013] FIG. 6 shows an example of a gene-protein-reaction
association for trios-phosphate isomerase.
[0014] FIGS. 7-1 through 7-35 show a metabolic network of adipocyte
metabolism.
[0015] FIG. 8 shows muscle contraction in a myocyte metabolic
model.
[0016] FIGS. 9-1 through 9-35 show a metabolic network of myocyte
metabolism.
[0017] FIGS. 10-1 through 10-70 show a metabolic network of coupled
adipoctye-myocyte metabolism.
[0018] FIG. 11 shows triacylglycerol degradation in an adipocyte
model.
[0019] FIG. 12 shows the impairment of muscle contraction as a
result of lactate accumulation during anaerobic exercise. Time is
in arbitrary unit. Concentration and yield of lactate production
are in mol/mol glucose.
[0020] FIG. 13 shows glycogen utilization versus (highlighted on
the left) glucose utilization (highlighted on the right) in
myocyte.
DETAILED DESCRIPTION OF THE INVENTION
[0021] The present invention provides in silico models that
describe the interconnections between genes in the Homo sapiens
genome and their associated reactions and reactants. The invention
also provides in silica models that describe interconnections
between different biochemical networks within a cell as well as
between cells. The interconnections among different biochemical
networks between cells can describe interactions between, for
example, groups of cells including cells within different
locations, tissues, organs or between cells carrying out different
functions of a multicellular organism. Therefore, the models can be
used to simulate different aspects of the cellular behavior of a
cell derived from a multicellular organism, including a human cell,
as well as be used to simulate different aspects of cellular
behavioral interactions of groups of cells. Such groups of cells
include, for example, eukaryotic cells, such as those of the same
tissue type or colonies of prokaryotic cells, or different types of
eukaryotic cells derived from the same or different tissue types
from a multicellular organism. The different aspects of cellular
behavior, including cellular behavioral interactions, can be
simulated under different normal, pathological and therapeutic
conditions. thereby providing valuable information for therapeutic,
diagnostic and research applications. One advantage of the models
of the invention is that they provide a holistic approach to
simulating and predicting the activity of multicellular organisms,
cellular interactions and individual cells, including the activity
of Homo sapiens cells. Therefore, the models and methods can be
used to simulate the activity of multiple interacting cells,
including organs, physiological systems and whole body metabolism
for practical diagnostic and therapeutic purposes.
[0022] In one embodiment, the invention is exemplified by reference
to a metabolic model of a Homo sapien cell. This in silica model of
an eukaryotic cell describes the cellular behavior resulting from
two or more interacting networks because it can contain metabolic,
regulatory and other network interactions, as described below. The
models and methods of the invention applicable to the production
and use of a cellular model containing two or more interacting
networks also are applicable to the production and use of a
multi-network model where the two or more networks are separated
between compartments such as cells or tissues of a multicellular
organism. Therefore. a Homo sapien or other eukaryotic cell model
of the invention exemplifies application of the models and methods
of the invention to models that describe the interaction of
multiple biochemical networks between and among cells of a tissue,
organ, physiological system or whole organism.
[0023] In another embodiment, the Homo sapiens metabolic models of
the invention can be used to determine the effects of changes from
aerobic to anaerobic conditions, such as occurs in skeletal muscles
during exercise or in tumors, or to determine the effect of various
dietary changes. The Homo sapiens metabolic models can also be used
to determine the consequences of genetic defects, such as
deficiencies in metabolic enzymes such as phosphofructokinase,
phosphoglycerate kinase, phosphoglycerate mutase, lactate
dehydrogenase and adenosine deaminase.
[0024] In a further embodiment, the invention provides a model of
multicellular interactions that includes the network
reconstruction, characteristics and simulation performance of an
integrated two cell model of human adipocyte and myocyte cells.
This multicellular model also included an intra-system biochemical
network for extracellular physiological systems. The model was
generated by reconstructing each of the component biochemical
networks within the cells and combining them together with the
addition of the intra-system biochemical network and achieved
accurate predictive performance of the two cell types under
different physiological conditions. Such multicellular metabolic
models can be employed for the same determinations as described
above for the Homo sapiens metabolic models. The determinations can
be performed at the cellular, tissue, physiological system or
organism level.
[0025] The multicellular and Homo sapiens metabolic models also can
be used to choose appropriate targets for drug design. Such targets
include genes, proteins or reactants, which when modulated
positively or negatively in a simulation produce a desired
therapeutic result. The models and methods of the invention can
also be used to predict the effects of a therapeutic agent or
dietary supplement on a cellular function of interest. Likewise,
the models and methods can be used to predict both desirable and
undesirable side effects of the therapeutic agent on an
interrelated cellular function in the target cell, as well as the
desirable and undesirable effects that may occur in other cell
types. Thus, the models and methods of the invention can make the
drug development process more rapid and cost effective than is
currently possible.
[0026] The multicellular and Homo sapiens metabolic models also can
be used to predict or validate the assignment of particular
biochemical reactions to the enzyme-encoding genes found in the
genome, and to identify the presence of reactions or pathways not
indicated by current genomic data. Thus, the models can be used to
guide the research and discovery process, potentially leading to
the identification of new enzymes, medicines or metabolites of
clinical importance.
[0027] The models of the invention are based on a data structure
relating a plurality of reactants to a plurality of reactions,
wherein each of the reactions includes a reactant identified as a
substrate of the reaction, a reactant identified as a product of
the reaction and a stoichiometric coefficient relating the
substrate and the product. The reactions included in the data
structure can be those that are common to all or most cells or to a
particular type or species of cell, including Homo sapiens cells,
such as core metabolic reactions, or reactions specific for one or
more given cell type.
[0028] As used herein, the term "reaction" is intended to mean a
conversion that consumes a substrate or forms a product that occurs
in or by a cell. The term can include a conversion that occurs due
to the activity of one or more enzymes that are genetically encoded
by a genome of the cell. The term can also include a conversion
that occurs spontaneously in a cell. When used in reference to a
Homo sapiens reaction, the term is intended to mean a conversion
that consumes a substrate or forms a product that occurs in or by a
Homo sapiens cell. Conversions included in the term include, for
example, changes in chemical composition such as those due to
nucleophilic or electrophilic addition, nucleophilic or
electrophilic substitution, elimination, isomerization,
deamination, phosphorylation, methylation, reduction, oxidation or
changes in location such as those that occur due to a transport
reaction that moves a reactant from one cellular compartment to
another. In the case of a transport reaction, the substrate and
product of the reaction can be chemically the same and the
substrate and product can be differentiated according to location
in a particular cellular compartment. Thus, a reaction that
transports a chemically unchanged reactant from a first compartment
to a second compartment has as its substrate the reactant in the
first compartment and as its product the reactant in the second
compartment. It will be understood that when used in reference to
an in silico model or data structure, a reaction is intended to be
a representation of a chemical conversion that consumes a substrate
or produces a product.
[0029] As used herein, the term "reactant" is intended to mean a
chemical that is a substrate or a product of a reaction that occurs
in or by a cell. The term can include substrates or products of
reactions performed by one or more enzymes encoded by a genome,
reactions occurring in cells or organisms that are performed by one
or more non-genetically encoded macromolecule, protein or enzyme,
or reactions that occur spontaneously in a cell. When used in
reference to a Homo sapiens reactant, the term is intended to mean
a chemical that is a substrate or product of a reaction that occurs
in or by a Homo sapiens cell. Metabolites are understood to be
reactants within the meaning of the term. It will be understood
that when used in reference to an in silico model or data
structure, a reactant is intended to be a representation of a
chemical that is a substrate or a product of a reaction that occurs
in or by a cell.
[0030] As used herein the term "substrate" is intended to mean a
reactant that can be convened to one or more products by a
reaction. The term can include, for example, a reactant that is to
be chemically changed due to nucleophilic or electrophilic
addition, nucleophilic or electrophilic substitution, elimination,
isomerization, deamination, phosphorylation, methylation,
reduction, oxidation or that is to change location such as by being
transported across a membrane or to a different compartment.
[0031] As used herein, the term "product" is intended to mean a
reactant that results from a reaction with one or more substrates.
The term can include, for example, a reactant that has been
chemically changed due to nucleophilic or electrophilic addition,
nucleophilic or electrophilic substitution, elimination,
isomerization, deamination, phosphorylation, methylation, reduction
or oxidation or that has changed location such as by being
transported across a membrane or to a different compartment.
[0032] As used herein, the term "stoichiometric coefficient" is
intended to mean a numerical constant correlating the number of one
or more reactants and the number of one or more products in a
chemical reaction. Typically, the numbers are integers as they
denote the number of molecules of each reactant in an elementally
balanced chemical equation that describes the corresponding
conversion. However, in some cases the numbers can take on
non-integer values, for example, when used in a lumped reaction or
to reflect empirical data.
[0033] As used herein, the term "plurality," when used in reference
to reactions or reactants including Homo sapiens reactions or
reactants, is intended to mean at least 2 reactions or reactants.
The term can include any number of reactions or reactants in the
range from 2 to the number of naturally occurring reactants or
reactions for a particular of cell or cells. Thus, the term can
include, for example, at least 10, 20, 30, 50, 100, 150, 200, 300,
400, 500, 600 or more reactions or reactants. The number of
reactions or reactants can be expressed as a portion of the total
number of naturally occurring reactions for a particular cell or
cells including a Homo sapiens cell or cells, such as at least 20%,
30%, 50%, 60%, 75%, 90%, 95% or 98% of the total number of
naturally occurring reactions that occur in a particular Homo
sapiens cell.
[0034] Similarly, the term "plurality," when used in reference to
data structures, is intended to mean at least 2 data structures.
The term can include any number of data structures in the range
from 2 to the number of naturally occurring biochemical networks
for a particular subsystem, system, intracellular system, cellular
compartment, organelle, extra-cellular space, cytosol,
mitochondrion, nucleus, endoplasmic reticulum, group of cells,
tissue, organ or organism. Therefore. the term can include, for
example, at least about 3, 4, 5, 6, 7, 8, 9, 10, 25, 20, 25, 50,
100 or more biochemical networks. The term also can be expressed as
a portion of the total number of naturally occurring networks for
any of the particular categories above occurring in prokaryotic or
eukaryotic cells including Homo sapiens.
[0035] As used herein, the term "data structure" is intended to
mean a physical or logical relationship among data elements,
designed to support specific data manipulation functions. The term
can include, for example, a list of data elements that can be added
combined or otherwise manipulated such as a list of representations
for reactions from which reactants can be related in a matrix or
network. The term can also include a matrix that correlates data
elements from two or more lists of information such as a matrix
that correlates reactants to reactions. Information included in the
term can represent, for example, a substrate or product of a
chemical reaction, a chemical reaction relating one or more
substrates to one or more products. a constraint placed on a
reaction, or a stoichiometric coefficient.
[0036] As used herein, the term "constraint" is intended to mean an
upper or lower boundary for a reaction. A boundary can specify a
minimum or maximum flow of mass, electrons or energy through a
reaction. A boundary can further specify directionality of a
reaction. A boundary can be a constant value such as zero.
infinity, or a numerical value such as an integer. Alternatively, a
boundary can be a variable boundary value as set forth below.
[0037] As used herein, the term "variable." when used in reference
to a constraint is intended to mean capable of assuming any of a
set of values in response to being acted upon by a constraint
function. The term "function," when used in the context of a
constraint, is intended to be consistent with the meaning of the
term as it is understood in the computer and mathematical arts, A
function can be binary such that changes correspond to a reaction
being off or on. Alternatively, continuous functions can be used
such that changes in boundary values correspond to increases or
decreases in activity. Such increases or decreases can also be
binned or effectively digitized by a function capable of converting
sets of values to discreet integer values. A function included in
the term can correlate a boundary value with the presence, absence
or amount of a biochemical reaction network participant such as a
reactant, reaction, enzyme or gene. A function included in the term
can correlate a boundary value with an outcome of at least one
reaction in a reaction network that includes the reaction that is
constrained by the boundary limit. A function included in the term
can also correlate a boundary value with an environmental condition
such as time, pH, temperature or redox potential.
[0038] As used herein, the term "activity," when used in reference
to a reaction, is intended to mean the amount of product produced
by the reaction, the amount of substrate consumed by the reaction
or the rate at which a product is produced or a substrate is
consumed. The amount of product produced by the reaction, the
amount of substrate consumed by the reaction or the rate at which a
product is produced or a substrate is consumed can also be referred
to as the flux for the reaction.
[0039] As used herein, the term "activity," when used in reference
to a Homo sapiens cell or a multicellular interaction, is intended
to mean the magnitude or rate of a change from an initial state to
a final state. The term can include, for example, the amount of a
chemical consumed or produced by a cell, the rate at which a
chemical is consumed or produced by a cell, the amount or rate of
growth of a cell or the amount of or rate at which energy, mass or
electrons flow through a particular subset of reactions.
[0040] The invention provides a computer readable medium, having a
data structure relating a plurality of Homo sapiens reactants to a
plurality of Homo sapiens reactions, wherein each of the Homo
sapiens reactions includes a reactant identified as a substrate of
the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating the substrate and the
product.
[0041] Also provided is a computer readable medium or media having:
(a) a first data structure relating a plurality of reactants to a
plurality of reactions from a first cell, each of said reactions
comprising a reactant identified as a substrate of the reaction, a
reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product; (b) a second data structure relating a plurality of
reactants to a plurality of reactions from a second cell, each of
said reactions comprising a reactant identified as a substrate
oldie reaction, a reactant identified as a product oldie reaction
and a stoichiometric coefficient relating said substrate and said
product; (c) a third data structure relating a plurality of
intra-system reactants to a plurality of intra-system reactions
between said first and second cells, each of said intra-system
reactions comprising a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product; (c) a constraint set for said plurality of reactions for
said first, second and third data structures, and (d) commands for
determining at least one flux distribution that minimizes or
maximizes an objective function when said constraint set is applied
to said first and second data structures, wherein said at least one
flux distribution is predictive of a physiological function of said
first and second cells.
[0042] Depending on the application, the plurality of reactions for
any of a multicellular, multi-network or single cell model or
method of the invention, including a Homo sapiens cell model or
method, can include reactions selected from core metabolic
reactions or peripheral metabolic reactions. As used herein, the
term "core," when used in reference to a metabolic pathway, is
intended to mean a metabolic pathway selected from
glycolysis/gluconeogenesis, the pentose phosphate pathway (PPP),
the tricarboxylic acid (TCA) cycle, glycogen storage, electron
transfer system (ETS). the malate/aspartate shuttle, the glycerol
phosphate shuttle, and plasma and mitochondrial membrane
transporters. As used herein, the term "peripheral," when used in
reference to a metabolic pathway, is intended to mean a metabolic
pathway that includes one or more reactions that are not a part of
a core metabolic pathway.
[0043] A plurality of reactants can be related to a plurality of
reactions in any data structure that represents, for each reactant,
the reactions by which it is consumed or produced. Thus, the data
structure, which is referred to herein as a "reaction network data
structure," serves as a representation of a biological reaction
network or system. An example of a reaction network that can be
represented in a reaction network data structure of the invention
is the collection of reactions that constitute the core metabolic
reactions of Homo sapiens, or the metabolic reactions of a skeletal
muscle cell, as shown in the Examples. Further examples of reaction
networks that can be represented in a reaction network data
structure of the invention are the collection of reactions that
constitute the core metabolic reactions and the triacylglycerol
(TAG) biosynthetic pathways of an adipocyte cell; the core
metabolic reactions and the energy and contractile reactions of a
myocyte cell, and the intra-system reactions that supply buffering
functions of the kidney.
[0044] The choice of reactions to include in a particular reaction
network data structure, from among all the possible reactions that
can occur in multicellular organisms or among multicellular
interactions, including human cells, depends on the cell type or
types and the physiological, pathological or therapeutic condition
being modeled, and can be determined experimentally or from the
literature, as described further below.
[0045] The reactions to be included in a particular network data
structure of a multicellular interaction can be determined
experimentally using, for example, gene or protein expression
profiles, where the molecular characteristics of the cell can be
correlated to the expression levels. The expression or lack of
expression of genes or proteins in a cell type can be used in
determining whether a reaction is included in the model by
association to the expressed gene(s) and or protein(s). Thus, it is
possible to use experimental technologies to determine which genes
and/or proteins are expressed in a specific cell type, and to
further use this information to determine which reactions are
present in the cell type of interest. In this way a subset of
reactions from all of those reactions that can occur in human cells
are selected to comprise the set of reactions that represent a
specific cell type. cDNA expression profiles have been demonstrated
to be useful., for example, for classification of breast cancer
cells (Sorlie et al., Proc. Natl. Acad. Sci. U.S.A.
98(19):10869-10874 (2001)).
[0046] The methods and models of the invention can be applied to
any multicellular interaction as well as to any Homo sapiens cell
type at any stage of differentiation, including, for example,
embryonic stem cells, hematopoietic stein cells, differentiated
hematopoietic cells, skeletal muscle cells, cardiac muscle cells,
smooth muscle cells, skin cells, nerve cells, kidney cells,
pulmonary cells, liver cells, adipocytes and endocrine cells (e.g.
beta islet cells of the pancreas, mammary gland cells, adrenal
cells, and other specialized hormone secreting cells). Similarly,
the methods and models of the invention can be applied to any
interaction between any of these cell types, including two or more
of the same cell type or two or more different cell types.
Described below in Example IV is an example of the interactions
that occur between myocyte cells and adipocyte cells during
different physiological conditions.
[0047] The methods and models of the invention can be applied to
normal cells, pathological cells as well as to combinations of
interactions between normal cells, interactions between
pathological cells or interactions between normal and pathological
cells. Normal cells that exhibit a variety of physiological
activities of interest, including homeostasis, proliferation.
differentiation, apoptosis, contraction and motility, can be
modeled. Pathological cells can also be modeled, including cells
that reflect genetic or developmental abnormalities, nutritional
deficiencies, environmental assaults, infection (such as by
bacteria, viral, protozoan or fungal agents), neoplasia, aging,
altered immune or endocrine function, tissue damage, or any
combination of these factors. The pathological cells can be
representative of any type of pathology, such as a human pathology,
including, for example, various metabolic disorders of
carbohydrate, lipid or protein metabolism, obesity, diabetes,
cardiovascular disease, fibrosis, various cancers, kidney failure,
immune pathologies, neurodegenerative diseases, and various
monogenetic metabolic diseases described in the Online Mendelian
Inheritance in Man database (Center for Medical Genetics, Johns
Hopkins University (Baltimore, Md.) and National Center for
Biotechnology Information, National Library of Medicine (Bethesda,
Md.)).
[0048] The methods and models of the invention can also be applied
to cells or organisms undergoing therapeutic perturbations, such as
cells treated with drugs that target participants in a reaction
network or cause an effect on an interactive reaction network,
cells or tissues treated with gene-based therapeutics that increase
or decrease expression of an encoded protein, and cells or tissues
treated with radiation. As used herein, the term "drug" refers to a
compound of any molecular nature with a known or proposed
therapeutic function, including, for example, small molecule
compounds, peptides and other macromolecules, peptidomimetics and
antibodies, any of which can optionally be tagged with cytostatic,
targeting or detectable moieties. The term "gene-based therapeutic"
refers to nucleic acid therapeutics, including, for example,
expressible genes with normal or altered protein activity,
antisense compounds, ribozymes, DNAzymes, RNA interference
compounds (RNAi) and the like. The therapeutics can target any
reaction network participant, in any cellular location, including
participants in extracellular, cell surface, cytoplasmic,
mitochondrial and nuclear locations. Experimental data that are
gathered on the response of cells, tissues, or interactions
thereof, to therapeutic treatment, such as alterations in gene or
protein expression profiles, can be used to tailor a network or a
combination of networks for a pathological state of a particular
cell type.
[0049] The methods and models of the invention can be applied to
cells, tissues and physiological systems, including Homo sapiens
cells, tissues and physiological systems, as they exist in any
form, such as in primary cell isolates or in established cell
lines, or in the whole body, in intact organs or in tissue
explants. Accordingly, the methods and models can take into account
intercellular communications and/or inter-organ communications, the
effect of adhesion to a substrate or neighboring cells (such as a
stem cell interacting with mesenchymal cells or a cancer cell
interacting with its tissue microenvironment, or beta-islet cells
without normal stroma), and other interactions relevant to
multicellular systems.
[0050] The reactants to be used in a reaction network data
structure of the invention can be obtained from or stored in a
compound database. As used herein, the term "compound database" is
intended to mean a computer readable medium or media containing a
plurality of molecules that includes substrates and products of
biological reactions. The plurality of molecules can include
molecules found in multiple organisms, thereby constituting a
universal compound database. Alternatively, the plurality of
molecules can be limited to those that occur in a particular
organism, thereby constituting an organism-specific compound
database. Each reactant in a compound database can be identified
according to the chemical species and the cellular compartment in
which it is present. Thus, for example, a distinction can be made
between glucose in the extracellular compartment versus glucose in
the cytosol. Additionally each of the reactants can be specified as
a metabolite of a primary or secondary metabolic pathway. Although
identification of a reactant as a metabolite of a primary or
secondary metabolic pathway does not indicate any chemical
distinction between the reactants in a reaction, such a designation
can assist in visual representations of large networks of
reactions.
[0051] As used herein, the term "compartment" is intended to mean a
subdivided region containing at least one reactant, such that the
reactant is separated from at least one other reactant in a second
region. A subdivided region included in the term can be correlated
with a subdivided region of a cell. Thus, a subdivided region
included in the term can be, for example, the intracellular space
of a cell; the extracellular space around a cell; the periplasmic
space, the interior space of an organelle such as a mitochondrium,
endoplasmic reticulum, Golgi apparatus, vacuole or nucleus; or any
subcellular space that is separated from another by a membrane or
other physical barrier. For example, a mitochondrial compartment is
a subdivided region of the intracellular space of a cell, which in
turn, is a subdivided region of a cell or tissue. A subdivided
region also can include, for example, different regions or systems
of a tissue,organ or physiological system of an organism.
Subdivided regions can also be made in order to create virtual
boundaries in a reaction network that are not correlated with
physical barriers. Virtual boundaries can be made for the purpose
of segmenting the reactions in a network into different
compartments or substructures.
[0052] As used herein, the term "substructure" is intended to mean
a portion of the information in a data structure that is separated
from other information in the data structure such that the portion
of information can be separately manipulated or analyzed. The term
can include portions subdivided according to a biological function
including, for example, information relevant to a particular
metabolic pathway such as an internal flux pathway. exchange flux
pathway, central metabolic pathway, peripheral metabolic pathway,
or secondary metabolic pathway. The term can include portions
subdivided according to computational or mathematical principles
that allow for a particular type of analysis or manipulation of the
data structure.
[0053] The reactions included in a reaction network data structure
can be obtained from a metabolic reaction database that includes
the substrates, products, and stoichiometry of a plurality of
metabolic reactions of Homo sapiens, other multicellular organisms
or single cell organisms that exhibit biochemical or physiological
interactions. The reactants in a reaction network data structure
can be designated as either substrates or products of a particular
reaction, each with a stoichiometric coefficient assigned to it to
describe the chemical conversion taking place in the reaction. Each
reaction is also described as occurring in either a reversible or
irreversible direction. Reversible reactions can either be
represented as one reaction that operates in both the forward and
reverse direction or be decomposed into two irreversible reactions,
one corresponding to the forward reaction and the other
corresponding to the backward reaction.
[0054] Reactions included in a reaction network data structure can
include intra-system or exchange reactions. Intra-system reactions
are the chemically and electrically balanced interconversions of
chemical species and transport processes. which serve to replenish
or drain the relative amounts of certain metabolites. These
intra-system reactions can be classified as either being
transformations or translocations. A transformation is a reaction
that contains distinct sets of compounds as substrates and
products, while a translocation contains reactants located in
different compartments. Thus a reaction that simply transports a
metabolite from the extracellular environment to the cytosol,
without changing its chemical composition is solely classified as a
translocation, while a reaction that takes an extracellular
substrate and converts it into a cytosolic product is both a
translocation and a transformation. Further, intra-system reactions
can include reactions representing one or more biochemical or
physiological functions of an independent cell, tissue, organ or
physiological system. For example, the buffering function of the
kidneys for the hematopoietic system and intra-cellular
environments can be represented as intra-system reactions and be
included in a multicellular interaction model either as an
independent reaction network or merged with one or more reaction
networks of the constituent cells.
[0055] Exchange reactions are those which constitute sources and
sinks, allowing the passage of metabolites into and out of a
compartment or across a hypothetical system boundary. These
reactions are included in a model for simulation purposes and
represent the metabolic demands placed on Homo sapiens. While they
may be chemically balanced in certain cases, they are typically not
balanced and can often have only a single substrate or product. As
a matter of convention the exchange reactions are further
classified into demand exchange and input/output exchange
reactions.
[0056] The metabolic demands placed on a multicellular or Homo
sapiens metabolic reaction network can be readily determined from
the dry weight composition of the cell, cells, tissue, organ or
organism which is available in the published literature or which
can be determined experimentally. The uptake rates and maintenance
requirements for Homo sapiens cells can also be obtained from the
published literature or determined experimentally.
[0057] Input/output exchange reactions are used to allow
extracellular reactants to enter or exit the reaction network
represented by a model of the invention. For each of the
extracellular metabolites a corresponding input/output exchange
reaction can be created. These reactions are always reversible with
the metabolite indicated as a substrate with a stoichiometric
coefficient of one and no products produced by the reaction. This
particular convention is adopted to allow the reaction to take on a
positive flux value (activity level) when the metabolite is being
produced or removed from the reaction network and a negative flux
value when the metabolite is being consumed or introduced into the
reaction network. These reactions will be further constrained
during the course of a simulation to specify exactly which
metabolites are available to the cell and which can be excreted by
the cell.
[0058] A demand exchange reaction is always specified as an
irreversible reaction containing at least one substrate. These
reactions are typically formulated to represent the production of
an intracellular metabolite by the metabolic network or the
aggregate production of many reactants in balanced ratios such as
in the representation of a reaction that leads to biomass
formation, also referred to as growth.
[0059] A demand exchange reactions can be introduced for any
metabolite in a model of the invention. Most commonly these
reactions are introduced for metabolites that are required to be
produced by the cell for the purposes of creating a new cell such
as amino acids, nucleotides, phospholipids, and other biomass
constituents, or metabolites that are to be produced for
alternative purposes. Once these metabolites are identified. a
demand exchange reaction that is irreversible and specifies the
metabolite as a substrate with a stoichiometric coefficient of
unity can be created. With these specifications, if the reaction is
active it leads to the net production of the metabolite by the
system meeting potential production demands. Examples of processes
that can be represented as a demand exchange reaction in a reaction
network data structure and analyzed by the methods of the invention
include, for example, production or secretion of an individual
protein; production or secretion of an individual metabolite such
as an amino acid, vitamin. nucleoside, antibiotic or surfactant;
production of ATP for extraneous energy requiring processes such as
locomotion or muscle contraction; or formation of biomass
constituents.
[0060] In addition to these demand exchange reactions that are
placed on individual metabolites, demand exchange reactions that
utilize multiple metabolites in defined stoichiometric ratios can
be introduced. These reactions are referred to as aggregate demand
exchange reactions. An example of an aggregate demand reaction is a
reaction used to simulate the concurrent growth demands or
production requirements associated with cell growth that are placed
on a cell, for example, by simulating the formation of multiple
biomass constituents simultaneously at a particular cellular or
organismic growth rate.
[0061] A specific reaction network is provided in FIG. 1 to
exemplify the above-described reactions and their interactions. The
reactions can be represented in the exemplary data structure shown
in FIG. 3 as set forth below. The reaction network, shown in FIG.
1, includes intra-system reactions that occur entirely within the
compartment indicated by the shaded oval such as reversible
reaction R.sub.2 which acts on reactants B and G and reaction
R.sub.3 which converts one equivalent of B to 2 equivalents of F.
The reaction network shown in FIG. 1 also contains exchange
reactions such as input/output exchange reactions A.sub.x1 and
E.sub.x1, and the demand exchange reaction, which represents growth
in response to the one equivalent of D and one equivalent of F.
Other intra-system reactions include R.sub.1 which is a
translocation and transformation reaction that translocates
reactant A into the compartment and transforms it to reactant G and
reaction R.sub.6 which is a transport reaction that translocates
reactant E out of the compartment.
[0062] A reaction network can be represented as a set of linear
algebraic equations which can be presented as a stoichiometric
matrix S, with S being an m x n matrix where m corresponds to the
number of reactants or metabolites and n corresponds to the number
of reactions taking place in the network. An example of a
stoichiometric matrix representing the reaction network of FIG. 1
is shown in FIG. 3. As shown in FIG. 3, each column in the matrix
corresponds to a particular reaction n, each row corresponds to a
particular reactant m, and each S.sub.mn element corresponds to the
stoichiometric coefficient of the reactant m in the reaction
denoted n. The stoichiometric matrix includes intra-system
reactions such as R.sub.2 and R.sub.3 which are related to
reactants that participate in the respective reactions according to
a stoichiometric coefficient having a sign indicative of whether
the reactant is a substrate or product of the reaction and a value
correlated with the number of equivalents of the reactant consumed
or produced by the reaction. Exchange reactions such as -E.sub.xt
and -A.sub.xt are similarly correlated with a stoichiometric
coefficient. As exemplified by reactant E, the same compound can be
treated separately as an internal reactant (E) and an external
reactant (E.sub.external) such that an exchange reaction (R.sub.6)
exporting the compound is correlated by stoichiometric coefficients
of -1 and 1, respectively. However, because the compound is treated
as a separate reactant by virtue of its compartmental location, a
reaction, such as R.sub.5, which produces the internal reactant (E)
but does not act on the external reactant (E.sub.external) is
correlated by stoichiometric coefficients of 1 and 0, respectively.
Demand reactions such as V.sub.growth can also be included in the
stoichiometric matrix being correlated with substrates by an
appropriate stoichiometric coefficient.
[0063] As set forth in further detail below, a stoichiometric
matrix provides a convenient format for representing and analyzing
a reaction network because it can be readily manipulated and used
to compute network properties, for example, by using linear
programming or general convex analysis. A reaction network data
structure can take on a variety of formats so long as it is capable
of relating reactants and reactions in the manner exemplified above
for a stoichiometric matrix and in a manner that can be manipulated
to determine an activity of one or more reactions using methods
such as those exemplified below. Other examples of reaction network
data structures that are useful in the invention include a
connected graph, list of chemical reactions or a table or reaction
equations.
[0064] A reaction network data structure can be constructed to
include all reactions that are involved in metabolism occurring
during the interaction of two or more cells. Homo sapiens cell
metabolism or any portion thereof. A portion of an organisms
metabolic reactions that can be included in a reaction network data
structure of the invention includes, for example, a central
metabolic pathway such as glycolysis, the TCA cycle, the PPP or
ETS; or a peripheral metabolic pathway such as amino acid
biosynthesis, amino acid degradation, purine biosynthesis,
pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism,
vitamin or cofactor biosynthesis, transport processes and
alternative carbon source catabolism. Examples of individual
pathways within the peripheral pathways are set forth in Table 1.
Other examples of portions of metabolic reactions that can be
included in a reaction network data structure of the invention
include, for example, TAG biosynthesis, muscle contraction
requirements, bicarbonate buffer system and/or ammonia buffer
system. Specific examples of these and other reactions are
described further below and in the Examples.
[0065] Depending upon a particular application, a reaction network
data structure can include a plurality of Homo sapiens reactions
including any or all of the reactions listed in Table 1, Similarly,
a reaction network data structure also can include the reactions
set forth in Examples I-IV and include, for example, single
reaction networks, multiple reaction networks that interact within
a cell as well as multiple reaction networks that interact between
cells or physiological systems.
[0066] For some applications, it can be advantageous to use a
reaction network data structure that includes a minimal number of
reactions to achieve a particular Homo sapiens activity or activity
of a multicellular interaction under a particular set of
environmental conditions. A reaction network data structure having
a minimal number of reactions can be identified by performing the
simulation methods described below in an iterative fashion where
different reactions or sets of reactions are systematically removed
and the effects observed. Accordingly, the invention provides a
computer readable medium, containing a data structure relating a
plurality of Homo sapiens reactants to a plurality of Homo sapiens
reactions, wherein the plurality of Homo sapiens reactions contains
at least 65 reactions. For example, the core metabolic reaction
database shown in Tables 2 and 3 contains 65 reactions, and is
sufficient to simulate aerobic and anaerobic metabolism on a number
of carbon sources, including glucose. Similarly, the invention
provides a computer readable medium containing a data structure
relating a plurality of reactants or multicellular interactions to
a plurality of reactions from multicellular interactions, wherein
the reactions contain at least 430 for a two cell interaction. Such
reactions between multicellular interactions are exemplified in
Table 11, for example.
[0067] Depending upon the particular cell type or types, the
physiological, pathological or therapeutic conditions being tested,
the desired activity and the number of cellular interactions of a
model or method of the invention, a reaction network data structure
can contain smaller numbers of reactions such as at least 200, 150,
100 or 50 reactions. A reaction network data structure having
relatively few reactions can provide the advantage of reducing
computation time and resources required to perform a simulation.
When desired, a reaction network data structure having a particular
subset of reactions can be made or used in which reactions that are
not relevant to the particular simulation are omitted.
Alternatively, larger numbers of reactions can be included in order
to increase the accuracy or molecular detail of the methods of the
invention or to suit a particular application. Thus, a reaction
network data structure can contain at least 300, 350, 400, 450,
500, 550, 600 or more reactions up to the number of reactions that
occur in or by multicellular interactions, including Homo sapiens,
or that are desired to simulate the activity of the full set of
reactions occurring in multicellular interactions, including Homo
sapiens. A reaction network data structure that is substantially
complete with respect to the metabolic reactions of a multicellular
organism, including Homo sapiens, provides an advantage of being
relevant to a wide range of conditions to be simulated, whereas
those with smaller numbers of metabolic reactions are specific to a
particular subset of conditions to be simulated.
[0068] A Homo sapiens reaction network data structure can include
one or more reactions that occur in or by Homo sapiens and that do
not occur, either naturally or following manipulation, in or by
another organism, such as Saccharomyes cerevisiae, it is understood
that a Homo sapiens reaction network data structure of a particular
cell type can also include one or more reactions that occur in
another cell type. Addition of such heterologous reactions to a
reaction network data structure of the invention can be used in
methods to predict the consequences of heterologous gene transfer
and protein expression, for example, when designing in vivo and ex
vivo gene therapy approaches. Similarly, reaction networks for a
multicellular interactions also can include one or more reactions
that occur entirely within the species of origin of the cellular
interactions or can contain one or more heterologous reactions from
one or more different species.
[0069] The reactions included in a reaction network data structure
of the invention can be metabolic reactions. A reaction network
data structure can also be constructed to include other types of
reactions such as regulatory reactions, signal transduction
reactions, cell cycle reactions, reactions controlling
developmental processes, reactions involved in apoptosis, reactions
involved in responses to hypoxia, reactions involved in responses
to cell-cell or cell-substrate interactions, reactions involved in
protein synthesis and regulation thereof, reactions involved in
gene transcription and translation, and regulation thereof, and
reactions involved in assembly of a cell and its subcellular
components.
[0070] A reaction network data structure or index of reactions used
in the data structure such as that available in a metabolic
reaction database, as described above, can be annotated to include
information about a particular reaction. A reaction can be
annotated to indicate, for example, assignment of the reaction to a
protein, macromolecule or enzyme that performs the reaction,
assignment of a gene(s) that codes for the protein, macromolecule
or enzyme, the Enzyme Commission (EC) number of the particular
metabolic reaction, a subset of reactions to which the reaction
belongs, citations to references from which information was
obtained, or a level of confidence with which a reaction is
believed to occur in Homo sapiens or other organism. A computer
readable medium or media of the invention can include a gene
database containing annotated reactions. Such information can be
obtained during the course of building a metabolic reaction
database or model of the invention as described below.
[0071] As used herein, the term "gene database" is intended to mean
a computer readable medium or media that contains at least one
reaction that is annotated to assign a reaction to one or more
macromolecules that perform the reaction or to assign one or more
nucleic acid that encodes the one or more macromolecules that
perform the reaction. A gene database can contain a plurality of
reactions, some or all of which are annotated. An annotation can
include, for example, a name for a macromolecule; assignment of a
function to a macromolecule; assignment of an organism that
contains the macromolecule or produces the macromolecule:
assignment of a subcellular location for the macromolecule;
assignment of conditions under which a macromolecule is regulated
with respect to performing a reaction, being expressed or being
degraded; assignment of a cellular component that regulates a
macromolecule; an amino acid or nucleotide sequence for the
macromolecule; a mRNA isoform, enzyme isoform, or any other
desirable annotation or annotation found for a macromolecule in a
genome database such as those that can he found in Genbank, a site
maintained by the NCBI (ncbi.nlm.gov). the Kyoto Encyclopedia of
Genes and Genomes (KEGG) (www.genome.ad.jp/kegg/), the protein
database SWISS-PROT (ca.expasy.org/sprot/), the LocusLink database
maintained by the NCBI (www.ncbi.nlm.nih.gov/LocusLink/), the
Enzyme Nomenclature database maintained by G.P. Moss of Queen Mary
and Westfield College in the United Kingdom
(www.chem.qmw.ac.uk/iubmb/enzyme/).
[0072] A gene database of the invention can include a substantially
complete collection of genes or open reading frames in a
multicellular organism, including Homo sapiens, or a substantially
complete collection of the macromolecules encoded by the organism's
genome. Alternatively, a gene database can include a portion of
genes or open reading frames in an organism or a portion of the
macromolecules encoded by the organism's genome, such as the
portion that includes substantially all metabolic genes or
macromolecules. The portion can be at least 10%, 15%, 20%, 25%,
50%, 75%, 90% or 95% of the genes or open reading frames encoded by
the organism's genome, or the macromolecules encoded therein. A
gene database can also include macromolecules encoded by at least a
portion of the nucleotide sequence for the organism's genome such
as at least 10%, 15%. 20%, 25%, 50%, 75%, 90% or 95% of the
organism's genome. Accordingly, a computer readable medium or media
of the invention can include at least one reaction for each
macromolecule encoded by a portion of an organism's genome,
including a Homo sapiens genome.
[0073] An in silica model of multicellular interactions, including
a Homo sapiens model, of the invention can be built by an iterative
process which includes gathering information regarding particular
reactions to be added to a model, representing the reactions in a
reaction network data structure, and performing preliminary
simulations wherein a set of constraints is placed on the reaction
network and the output evaluated to identify errors in the network.
Errors in the network such as gaps that lead to non-natural
accumulation or consumption of a particular metabolite can be
identified as described below and simulations repeated until a
desired performance of the model is attained. An exemplary method
for iterative model construction is provided in Example I. For
multicellular interactions, an iterative process includes producing
one or more component reaction networks followed by combining the
components into a higher order multi-network system, as described
in Example IV. For example, components can include the central
metabolism reaction network and the cell specific reaction networks
such as TAG biosynthesis for adipocytes or muscle contraction for
myocytes. Combination of the central metabolism and the cell
specific reaction networks into a single model produces, for
example, a cell specific reaction network. Components also can
include the individual cell types, tissues, physiological systems
or intra-system reaction networks that are constituents of the
larger multicellular system. Combining these components into a
larger model produces, for example, a model describing the
relationships and interactions of the multicellular system together
with its various interactions.
[0074] Thus, the invention provides a method for making a data
structure relating a plurality of reactants to a plurality of
reactions in a computer readable medium or media. The method
includes the steps of: (a) identifying a plurality of reactions and
a plurality of reactants that are substrates and products of the
reactions; (b) relating the plurality of reactants to the plurality
of Homo sapiens reactions in a data structure, wherein each of the
reactions includes a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating the substrate and the product;
(c) making a constraint set for the plurality of reactions; (d)
providing an objective function; (e) determining at least one flux
distribution that minimizes or maximizes the objective function
when the constraint set is applied to the data structure, and (f)
if the at least one flux distribution is not predictive of
physiology, then adding a reaction to or deleting a reaction from
the data structure and repeating step (e). if the at least one flux
distribution is predictive of physiology, then storing the data
structure in a computer readable medium or media. The method can be
applied to multicellular interactions within or among single or
multicullar organisms, including Homo sapiens.
[0075] Information to be included in a data structure of the
invention can be gathered from a variety of sources including, for
example, annotated genome sequence information and biochemical
literature.
[0076] Sources of annotated human genome sequence information
include, for example, KEGG, SWISS-PROT, LocusLink, the Enzyme
Nomenclature database, the International Human Genome Sequencing
Consortium and commercial databases. KEGG contains a broad range of
information, including a substantial amount of metabolic
reconstruction. The genomes of 304 organisms can be accessed here,
with gene products grouped by coordinated functions, often
represented by a map (e.g., the enzymes involved in glycolysis
would be grouped together). The maps are biochemical pathway
templates which show enzymes connecting metabolites for various
parts of metabolism. These general pathway templates are customized
for a given organism by highlighting enzymes on a given template
which have been identified in the genome of the organism. Enzymes
and metabolites are active and yield useful information about
stoichiometry, structure. alternative names and the like, when
accessed.
[0077] SWISS-PROT contains detailed information about protein
function. Accessible information includes alternate gene and gene
product names, function, structure and sequence information,
relevant literature references, and the like.
[0078] LocusLink contains general information about the locus where
the gene is located and, of relevance, tissue specificity, cellular
location, and implication of the gene product in various disease
states.
[0079] The Enzyme Nomenclature database can be used to compare the
gene products of two organisms. Often the gene names for genes with
similar functions in two or more organisms are unrelated. When this
is the case, the E.C. (Enzyme Commission) numbers can be used as
unambiguous indicators of gene product function. The information in
the Enzyme Nomenclature database is also published in Enzyme
Nomenclature (Academic Press, San Diego, Calif., 1992) with
supplements to date, all found in the European Journal of
Biochemistry (Blackwell Science, Malden, Mass.).
[0080] Sources of biochemical information include, for example,
general resources relating to metabolism, resources relating
specifically to human metabolism, and resources relating to the
biochemistry, physiology and pathology of specific human cell
types.
[0081] Sources of general information relating to metabolism, which
were used to generate the human reaction databases and models
described herein, were J. G. Salway, Metabolism at a Glance.
2.sup.nd ed., Blackwell Science. Malden, Mass. (1999) and T. M.
Devlin, ed., Textbook of Biochemistry with Correlations, 4.sup.th
ed., John Wiley and Sons, New York, N.Y. (1997). Human
metabolism-specific resources included J. R. Bronk,
HumanMetabolism: Functional Diversity and Intregration, Addison
Wesley Longman, Essex, England (1999).
[0082] The literature used in conjunction with the skeletal muscle
metabolic models and simulations described herein included R.
Maughan et al., Biochemistry of Exercise and Training, Oxford
University Press, Oxford, England (1997), as well as references on
muscle pathology such as S. Carpenter et al., Pathology of Skeletal
Muscle, 2nd ed., Oxford University Press, Oxford, England (2001),
and more specific articles on muscle metabolism as may be found in
the Journal of Physiology (Cambridge University Press, Cambridge,
England).
[0083] In the course of developing an in silica model of metabolism
during or for multicellular interactions, the types of data that
can be considered include, for example, biochemical information
which is information related to the experimental characterization
of a chemical reaction, often directly indicating a protein(s)
associated with a reaction and the stoichiometry of the reaction or
indirectly demonstrating the existence of a reaction occurring
within a cellular extract; genetic information, which is
information related to the experimental identification and genetic
characterization of a gene(s) shown to code for a particular
protein(s) implicated in carrying out a biochemical event; genomic
information, which is information related to the identification of
an open reading frame and functional assignment, through
computational sequence analysis, that is then linked to a protein
performing a biochemical event; physiological information. which is
information related to overall cellular physiology, fitness
characteristics, substrate utilization, and phenotyping results,
which provide evidence of the assimilation or dissimilation of a
compound used to infer the presence of specific biochemical event
(in particular translocations): and modeling information, which is
information generated through the course of simulating activity of
cells, tissues or physiological systems using methods such as those
described herein which lead to predictions regarding the status of
a reaction such as whether or not the reaction is required to
fulfill certain demands placed on a metabolic network. Additional
information relevant to multicellular organisms that can be
considered includes, for example, cell type-specific or
condition-specific gene expression information, which can be
determined experimentally, such as by gene array analysis or from
expressed sequence tag (EST) analysis, or obtained from the
biochemical and physiological literature.
[0084] The majority of the reactions occurring in a multicellular
organism's reaction networks are catalyzed by enzymes/proteins,
which are created through the transcription and translation of the
genes found within the chromosome in the cell. The remaining
reactions occur either spontaneously or through non-enzymatic
processes. Furthermore, a reaction network data structure can
contain reactions that add or delete steps to or from a particular
reaction pathway. For example, reactions can be added to optimize
or improve performance of a model for multicellular interactions in
view of empirically observed activity. Alternatively, reactions can
be deleted to remove intermediate steps in a pathway when the
intermediate steps are not necessary to model flux through the
pathway. For example, if a pathway contains 3 nonbranched steps,
the reactions can be combined or added together to give a net
reaction, thereby reducing memory required to store the reaction
network data structure and the computational resources required for
manipulation of the data structure.
[0085] The reactions that occur due to the activity of gene-encoded
enzymes can be obtained from a genome database which lists genes
identified from genome sequencing and subsequent genome annotation.
Genome annotation consists of the locations of open reading frames
and assignment of function from homology to other known genes or
empirically determined activity. Such a genome database can be
acquired through public or private databases containing annotated
nucleic acid or protein sequences, including Homo sapiens
sequences. If desired, a model developer can perform a network
reconstruction and establish the model content associations between
the genes, proteins, and reactions as described, for example, in
Covert et al. Trends in Biochemical Sciences 26:179-186 (2001) and
Palsson, WO 00146405.
[0086] As reactions are added to a reaction network data structure
or metabolic reaction database, those having known or putative
associations to the proteins/enzymes which enable/catalyze the
reaction and the associated genes that code for these proteins can
be identified by annotation. Accordingly, the appropriate
associations for all of the reactions to their related proteins or
genes or both can be assigned. These associations can be used to
capture the non-linear relationship between the genes and proteins
as well as between proteins and reactions. In some cases one gene
codes for one protein which then perform one reaction. However,
often there are multiple genes which are required to create an
active enzyme complex and often there are multiple reactions that
can be carried out by one protein or multiple proteins that can
carry out the same reaction. These associations capture the logic
(i.e. AND or OR relationships) within the associations. Annotating
a metabolic reaction database with these associations can allow the
methods to be used to determine the effects of adding or
eliminating a particular reaction not only at the reaction level,
but at the genetic or protein level in the context of running a
simulation or predicting a multicellular interaction activity,
including Homo sapiens activity.
[0087] A reaction network data structure of the invention can be
used to determine the activity of one or more reactions in a
plurality of reactions occurring from multicellular interactions,
including a plurality of Homo sapiens reactions, independent of any
knowledge or annotation of the identity of the protein that
performs the reaction or the gene encoding the protein. A model
that is annotated with gene or protein identities can include
reactions for which a protein or encoding gene is not assigned.
While a large portion of the reactions in a cellular metabolic
network are associated with genes in the organism's genome, there
are also a substantial number of reactions included in a model for
which there are no known genetic associations. Such reactions can
be added to a reaction database based upon other information that
is not necessarily related to genetics such as biochemical or cell
based measurements or theoretical considerations based on observed
biochemical or cellular activity. For example, there are many
reactions that can either occur spontaneously or are not
protein-enabled reactions. Furthermore, the occurrence of a
particular reaction in a cell for which no associated proteins or
genetics have been currently identified can be indicated during the
course of model building by the iterative model building methods of
the invention.
[0088] The reactions in a reaction network data structure or
reaction database can be assigned to subsystems by annotation, if
desired. The reactions can be subdivided according to biological
criteria, such as according to traditionally identified metabolic
pathways (glycolysis, amino acid metabolism and the like) or
according to mathematical or computational criteria that facilitate
manipulation of a model that incorporates or manipulates the
reactions. Methods and criteria for subdviding a reaction database
are described in further detail in Schilling et al., J. Theor.
Biol. 203:249-283 (2000), and in Schuster et al., Bioinformatics
18:351-361 (2002). The use of subsystems can be advantageous for a
number of analysis methods, such as extreme pathway analysis, and
can make the management of model content easier, Although assigning
reactions to subsystems can be achieved without affecting the use
of the entire model for simulation, assigning reactions to
subsystems can allow a user to search for reactions in a particular
subsystem which may be useful in performing various types of
analyses. Therefore, a reaction network data structure can include
any number of desired subsystems including, for example, 2 or more
subsystems, 5 or more subsystems, 10 or more subsystems, 25 or more
subsystems or 50 or more subsystems.
[0089] The reactions in a reaction network data structure or
metabolic reaction database can be annotated with a value
indicating the confidence with which the reaction is believed to
occur in one or more cells of a multicellular interaction or in one
or more reaction networks within a cell such as a Homo sapiens
cell. The level of confidence can be, for example, a function of
the amount and form of supporting data that is available. This data
can come in various forms including published literature,
documented experimental results, or results of computational
analyses. Furthermore, the data can provide direct or indirect
evidence for the existence of a chemical reaction in a cell based
on genetic, biochemical, and/or physiological data.
[0090] The invention further provides a computer readable medium,
containing (a) a data structure relating a plurality Homo sapiens
reactants to a plurality of Homo sapiens reactions, wherein each of
the Homo sapiens reactions includes a reactant identified as a
substrate of the reaction, a reactant identified as a product of
the reaction and a stoichiometric coefficient relating the
substrate and the product, and (b) a constraint set for the
plurality of Homo sapiens reactions. Similarly, the computer
readable medium or media can relate a plurality of reactions to a
plurality of reactions within first and second cells and for an
intra-system between first and second interacting cells.
[0091] Constraints can be placed on the value of any of the fluxes
in the metabolic network using a constraint set. These constraints
can be representative of a minimum or maximum allowable flux
through a given reaction, possibly resulting from a limited amount
of an enzyme present. Additionally. the constraints can determine
the direction or reversibility of any of the reactions or transport
fluxes in the reaction network data structure. Based on the in viva
environment where multiple cells interact, such as in a human
organism, the metabolic resources available to the cell for
biosynthesis of essential molecules for can be determined. Allowing
the corresponding transport fluxes to be active provides the in
silica interaction between cells with inputs and outputs for
substrates and by-products produced by the metabolic network.
[0092] Returning to the hypothetical reaction network shown in FIG.
1, constraints can be placed on each reaction in the exemplary
format shown in FIG. 2, as follows. The constraints are provided in
a format that can be used to constrain the reactions of the
stoichiometric matrix shown in FIG. 3. The format for the
constraints used for a matrix or in linear programming can be
conveniently represented as a linear inequality such as
bj.ltoreq.vj.ltoreq.aj:j=1 . . . n (Eq. 1)
where v.sub.j is the metabolic flux vector, b, is the minimum flux
value and a.sub.j is the maximum flux value. Thus, a.sub.j can take
on a finite value representing a maximum allowable flux through a
given reaction or b.sub.j can take on a finite value representing
minimum allowable flux through a given reaction. Additionally, if
one chooses to leave certain reversible reactions or transport
fluxes to operate in a forward and reverse manner the flux may
remain unconstrained by setting b.sub.j to negative infinity and a,
to positive infinity as shown for reaction R.sub.2 in FIG. 2. If
reactions proceed only in the forward reaction b.sub.j is set to
zero while a.sub.j is set to positive infinity as shown for
reactions R.sub.1, R.sub.3, R.sub.4, R.sub.5, and R.sub.6 in FIG.
2. As an example, to simulate the event of a genetic deletion or
non-expression of a particular protein, the flux through all of the
corresponding metabolic reactions related to the gene or protein in
question are reduced to zero by setting a, and b, to be zero.
Furthermore, if one wishes to simulate the absence of a particular
growth substrate one can simply constrain the corresponding
transport fluxes that allow the metabolite to enter the cell to be
zero by setting a.sub.j and b.sub.jto be zero. On the other hand if
a substrate is only allowed to enter or exit the cell via transport
mechanisms, the corresponding fluxes can be properly constrained to
reflect this scenario.
[0093] The ability of a reaction to be actively occurring is
dependent on a large number of additional factors beyond just the
availability of substrates. These factors, which can be represented
as variable constraints in the models and methods of the invention
include, for example, the presence of cofactors necessary to
stabilize the protein/enzyme, the presence or absence of enzymatic
inhibition and activation factors, the active formation of the
protein/enzyme through translation of the corresponding mRNA
transcript, the transcription of the associated gene(s) or the
presence of chemical signals and/or proteins that assist in
controlling these processes that ultimately determine whether a
chemical reaction is capable of being carried out within an
organism. Of particular importance in the regulation of human cell
types is the implementation of paracrine and endocrine signaling
pathways to control cellular activities. In these cases a cell
secretes signaling molecules that may be carried far afield to act
on distant targets (endocrine signaling), or act as local mediators
(paracrine signaling). Examples of endocrine signaling molecules
include hormones such as insulin, while examples of paracrine
signaling molecules include neurotransmitters such as
acetylcholine. These molecules induce cellular responses through
signaling cascades that affect the activity of biochemical
reactions in the cell. Regulation can be represented in an in
silico Homo sapiens model by providing a variable constraint as set
forth below.
[0094] Thus, the invention provides a computer readable medium or
media, including (a) a data structure relating a plurality of Homo
sapiens reactants to a plurality of Homo sapiens reactions, wherein
each of the reactions includes a reactant identified as a substrate
of the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating the substrate and the
product, and wherein at least one of the reactions is a regulated
reaction; and (b) a constraint set for the plurality of reactions,
wherein the constraint set includes a variable constraint for the
regulated reaction. Additionally, the invention provides a computer
readable medium or media including data structures for two or more
cells and for an intra-system and a constraint set for the
plurality of reactions within the data structures that includes a
variable constraint for a regulated reaction.
[0095] As used herein, the term "regulated," when used in reference
to a reaction in a data structure, is intended to mean a reaction
that experiences an altered flux due to a change in the value of a
constraint or a reaction that has a variable constraint.
[0096] As used herein, the term "regulatory reaction" is intended
to mean a chemical conversion or interaction that alters the
activity of a protein. macromolecule or enzyme. A chemical
conversion or interaction can directly alter the activity of a
protein, macromolecule or enzyme such as occurs when the protein,
macromolecule or enzyme is post-translationally modified or can
indirectly alter the activity of a protein, macromolecule or enzyme
such as occurs when a chemical conversion or binding event leads to
altered expression of the protein, macromolecule or enzyme. Thus,
transcriptional or translational regulatory pathways can indirectly
alter a protein, macromolecule or enzyme or an associated reaction.
Similarly, indirect regulatory reactions can include reactions that
occur due to downstream components or participants in a regulatory
reaction network. When used in reference to a data structure or in
silica Homo sapiens model, for example, the term is intended to
mean a first reaction that is related to a second reaction by a
function that alters the flux through the second reaction by
changing the value of a constraint on the second reaction.
[0097] As used herein, the term "regulatory data structure" is
intended to mean a representation of an event, reaction or network
of reactions that activate or inhibit a reaction, the
representation being in a format that can be manipulated or
analyzed. An event that activates a reaction can be an event that
initiates the reaction or an event that increases the rate or level
of activity for the reaction. An event that inhibits a reaction can
be an event that stops the reaction or an event that decreases the
rate or level of activity for the reaction. Reactions that can be
represented in a regulatory data structure include, for example,
reactions that control expression of a macromolecule that in turn,
performs a reaction such as transcription and translation
reactions, reactions that lead to post translational modification
of a protein or enzyme such as phophorylation, dephosphorylation,
prenylation, methylation, oxidation or covalent modification,
reactions that process a protein or enzyme such as removal of a
pre- or pro-sequence, reactions that degrade a protein or enzyme or
reactions that lead to assembly of a protein or enzyme.
[0098] As used herein, the term "regulatory event" is intended to
mean a modifier of the flux through a reaction that is independent
of the amount of reactants available to the reaction, A
modification included in the term can be a change in the presence,
absence, or amount of an enzyme that performs a reaction, A
modifier included in the term can be a regulatory reaction such as
a signal transduction reaction or an environmental condition such
as a change in pH, temperature, redox potential or time. It will be
understood that when used in reference to an in silica Homo sapiens
model or data structure, or when used in reference to a model or
data structure for a multicellular interaction, a regulatory event
is intended to be a representation of a modifier of the flux
through a Homo sapiens reaction or reaction occurring in one or
more cells in a multicellular interaction that is independent of
the amount of reactants available to the reaction.
[0099] The effects of regulation on one or more reactions that
occur in Homo sapiens can be predicted using an in silica Homo
sapiens model or multicellular model of the invention. Regulation
can be taken into consideration in the context of a particular
condition being examined by providing a variable constraint for the
reaction in an in silica Homo sapiens model or multicellular model.
Such constraints constitute condition-dependent constraints. A data
structure can represent regulatory reactions as Boolean logic
statements (Reg-reaction). The variable takes on a value of I when
the reaction is available for use in the reaction network and will
take on a value of 1 if the reaction is restrained due to some
regulatory feature. A series of Boolean statements can then be
introduced to mathematically represent the regulatory network as
described for example in Covert et al. J. Theor. Biol. 213:73-88
(2001). For example, in the case of a transport reaction (A_in)
that imports metabolite A, where metabolite A inhibits reaction R2
as shown in FIG. 4, a Boolean rule can state that:
Reg-R2=IF NOT(A_in). (Eq. 2)
This statement indicates that reaction R2 can occur if reaction
A_in is not occurring (i.e. if metabolite A is not present).
Similarly, it is possible to assign the regulation to a variable A
which would indicate an amount of A above or below a threshold that
leads to the inhibition of reaction R2. Any function that provides
values for variables corresponding to each of the reactions in the
biochemical reaction network can be used to represent a regulatory
reaction or set of regulatory reactions in a regulatory data
structure. Such functions can include, for example, fuzzy logic,
heuristic rule-based descriptions, differential equations or
kinetic equations detailing system dynamics.
[0100] A reaction constraint placed on a reaction can be
incorporated into an in silico Homo sapiens model or mulicellular
model of interacting cells using the following general
equation:
(Reg-Reaction)*b.sub.j.ltoreq.v.sub.j.ltoreq.a.sub.j*(Reg-Reaction),
.A-inverted.=1 . . . n (Eq. 3)
[0101] For the example of reaction R2 this equation is written as
follows:
(0)*Reg-R2.ltoreq.R2.ltoreq.(.infin.)*Reg-R2. (Eq. 4)
[0102] Thus, during the course of a simulation, depending upon the
presence or absence of metabolite A in the interior of the cell
where reaction R2 occurs, the value for the upper boundary of flux
for reaction R2 will change from 0 to infinity, respectively.
[0103] With the effects of a regulatory event or network taken into
consideration by a constraint function and the condition-dependent
constraints set to an initial relevant value, the behavior of the
Homo sapiens reaction network or one or more reaction networks of a
multicellular interaction can be simulated for the conditions
considered as set forth below.
[0104] Although regulation has been exemplified above for the case
where a variable constraint is dependent upon the outcome of a
reaction in the data structure, a plurality of variable constraints
can he included in an in silico Homo sapiens model or other model
of multicellular interactions to represent regulation of a
plurality of reactions. Furthermore, in the exemplary case set
forth above, the regulatory structure includes a general control
stating that a reaction is inhibited by a particular environmental
condition. Using a general control of this type, it is possible to
incorporate molecular mechanisms and additional detail into the
regulatory structure that is responsible for determining the active
nature of a particular chemical reaction within an organism.
[0105] Regulation can also be simulated by a model of the invention
and used to predict a Homo sapiens physiological function without
knowledge of the precise molecular mechanisms involved in the
reaction network being modeled, Thus, the model can be used to
predict, in overall regulatory events or causal relationships that
are not apparent from in vivo observation of any one reaction in a
network or whose in vivo effects on a particular reaction are not
known. Such overall regulatory effects can include those that
result from overall environmental conditions such as changes in pH,
temperature, redox potential, or the passage of time.
[0106] As described previously and further below, the models and
method of the invention are applicable to a wide range of
multicellular interactions. The multicellular interactions include,
for example, interactions between prokaryotic cells such as colony
growth and chemotaxis. The multicellular interactions include, for
example, interactions between two or more eukaryotic cells such as
the concerted action of two or more cells of the same or different
cell type. A specific example of the concerted action of the same
cell type includes the combined output of the contractile activity
of myocytes. A specific example of the concerted action of
different cell types includes the energy production of adipocyte
cells and the contractile activity of myocyte cells based on the
consumption of energy available from the adipocyte cells.
Multicellular interactions also can include, for example,
interactions between host cells and a pathogen, such as a bacteria,
virus or worm. as well as symbiotic interactions between host cells
and microbes, for example. A symbiotic microbe can include, for
example, E. coli. Further examples of host and microbe interactions
include bacterial communities that reside in the skin and mouth and
the vagina flora, providing the host with a defense against
infections. Moreover, the models and methods of the invention also
can be used to reconstruction the reaction networks between a
plurality of dynamic multicellular interactions including, for
example, interactions between host cells or tissues, pathogen and
symbiotic microbe.
[0107] Multicellular interactions also include, for example,
interactions between cells of different tissues, different organs
and/or physiological systems as well as interactions between some
or all cells, tissues organs and/or physiological systems within a
multicellular organism. Specific examples of such interactions
include organismic homeostasis. signal transduction, the endocrine
system, the exocrine system, sensory transduction, secretion, the
hematopoietic system, the immune system, cell migration, cell
adherence, cell invasion and neuronal and synaptic transduction.
Numerous other multicellular interactions are well known in the art
and can similarly be reconstructed and simulated to predict an
activity thereof using the models and methods of the invention.
[0108] Given the teachings and guidance provided herein with
respect to the construction and use of multiple reaction networks
including, for example, the regulated and metabolic reaction
networks of a Homo sapiens cell, those skilled in the art will know
how to employ the models and methods of the invention for the
construction and use of any multicellular interaction. Specific
examples of such multicellular interactions are described above.
Other examples of multicellular interactions include, for example,
all interactions occurring between two or more cells such as those
cells set forth in Table 5 below. Such multicellular interactions
can occur between cells within the same or different physiological
category or functional characterization. Similarly, such
multicellular interactions also can occur between cells within the
same and between different physiological categories or functional
characterizations. The number and types of different cellular
interactions will be determined by the multicellular model being
produced using the methods of the invention.
[0109] Models of multicellular interactions also can include, for
example, interactions between cells of one or more tissues and
organs. The models and methods of the invention are applicable to
predict the activity of interactions between some or all cell types
of a tissue or organ. The models and methods of the invention also
can include reaction networks that include interactions between
some or all cell types of two or more tissues or organs. Specific
examples of tissues or organs and their respective cell types and
functions are shown below in Table 6. The models and methods of the
invention can include, for example, some or all of these
interactions to predict their respective activities. Similarly,
Table 7 exemplifies the cell types of a liver. Given the teachings
and guidance provided herein, the models and methods of the
invention can be used to construct an in silico reconstruction of
the reaction networks for some or all of these cell types to
predict some or all of the activities of the liver. Further, an in
silico reconstruction of reaction networks for some or all
multicellular interactions exemplified in Tables 5-7, including
those within and between tissues and organs, can be produced that
can be used to predict some or all activities of one or more
tissues or of an organism. Therefore, the invention provides for
the in silky) reconstruction of whole organisms, including human
organisms, tissues, cells and physical or physiological functions
performed by such cellular systems.
[0110] The invention also provides for the in silky reconstruction
of a plurality of reaction networks that interact to perform the
same or different activity. The plurality can be a small, medium or
large plurality and can reside within the same cell, different
cells or in different tissues or organisms. Specific examples of
such pluralities residing within the same cell include the reaction
networks exemplified below in Example IV for a myocyte or for an
adipocyte. Specific examples of such pluralities residing in
different cells or tissues include the reaction networks
exemplified below in Example IV for coupled adipocyte-myocyte
metabolism. Another example of interactions between different
reaction networks within different networks includes interactions
between pathogen and host cells.
[0111] Briefly, and as described previously, a computer readable
medium or media can be produced that includes a plurality of data
structures each relating a plurality of reactants to a plurality of
reactions from each cell within the multicellular interaction. The
reactions include a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating the substrate and said product
In a two cell interaction, including populations of two cell types,
the plurality of data structures can include a first data structure
and a second data structure corresponding to the reactions within
the two cells or populations of two cell types. The data structures
will describe the reaction networks for each cell.
[0112] For optimization of the multicellular interaction containing
two cells, a third data structure is particularly useful for
relating a plurality of intra-system reactants to a plurality of
intra-system reactions between the first and second cells. Each of
the intra-system reactions includes a reactant identified as a
substrate of the reaction, a reactant identified as a product of
the reaction and a stoichiometric coefficient relating the
substrate and said product. The inta-system data structure can be
included in the reconstruction as an independent data structure or
as a component of one or more data structures for either or both
cells within such a two cell interaction model. A specific example
of intra-system reactions represented by a third data structure is
shown in FIGS. 10-1 through 10-70 for the bicarbonate and ammonia
buffer systems employed in the two cell model describing adipocyte
and myocyte interactions.
[0113] As with the models and methods of the invention described
above and below, a computer readable medium or media describing a
multicellular interaction also will contain a constraint set for
the plurality of reactions for each of the first, second and third
data structures as well as commands for determining at least one
flux distribution that minimizes or maximizes an objective function
when said constraint set is applied to said first and second data
structures. The objective function can be, for example, those
objective functions exemplified previously, those exemplified below
or in the Examples as well as various other object functions well
known to those skilled in the art given the teachings and guidance
provided herein. Solving the optimization problem by determining
one or more flux distribution will predict a physiological function
of occurring as a result of the interaction between the first and
second cells of the model.
[0114] Each of the first, second or third data structures can
include one or more reaction networks. For example, and with
reference to FIGS. 5-1 through 5-9, 6, 7-1 through 7-35, 8, 9-1
through 9-35, and 10-1 through 10-70, a reaction network for each
of the cells exemplified therein can be defined as the different
networks within each cell such as central metabolism and the cell
specific reactions. Applying this view, the adipocyte and myocyte
cells each contain at least two reaction networks. When combined
together with the intra-cellular reaction network and the exchange
reactions, the interactions of the two cells exemplified in FIG. 6
can be described by at least five different reaction networks. The
interactions of this two cell model can therefore be described
using at least five data structures. Alternatively, a reaction
network can be defined as all the networks within each cell. When
combined together with the intra-cellular reaction network and the
exchange reactions, the interactions of the exemplified adipocyte
and myocyte cells can be described by at least three different
reaction networks. One reaction network for each cell and one
reaction network for the intra-system reactions. Therefore, each of
the first, second or third data structures can consist of a
plurality of two or more reaction networks including, for example,
2. 3, 4. 5. 10, 20 or 25 or more as well as all integer numbers
between and above these exemplary numbers. Similarly. given the
teachings and guidance provided herein, the models and methods of
the invention can be generated and used to predict an activity
and/or physiological function of the intercellular network
interactions or the intracellular network interaction. The latter
interactions, for example, also predict an activity and/or a
physiological function of the interactions between two or more
cells including cells of different tissues, organs of a
multicellular organism or of a whole organism.
[0115] As with the number of reaction networks within a data
structure, the models and methods of the invention also can employ
greater than three data structures as exemplified above. For
example, the models and method of the invention can comprise one or
more fourth data structures having one or more fourth constraint
sets where each fourth data structure relates a plurality of
reactants to a plurality of reactions from a cell already included
in the model or from one or more third cells within the
multicellular interaction. Use of one or more fourth data
structures is particularly useful when reconstructing a
interactions between three or more interacting cells including a
large plurality of cells such as the cells within a tissue, organ,
physiological system or organism. Each of the reactions within such
fourth data structures include a reactant identified as a substrate
of the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating the substrate and said
product.
[0116] The number of fourth data structures can correspond to the
number of cells greater than the first and second cells of the
multicellular interaction and include, for example, a plurality of
data structures. As with the specific embodiment a two cell
interaction, the plurality of data structures for three or more
interacting cells can correspond to different cells within the
cellular interaction as well as correspond to different cell types
within the cellular interaction. The number of cells can include,
for example, at least 4 cells, 5 cells, 6 cells, 7 cells, 8 cells,
9 cells, 10 cells, 100 cells, 1000 cells, 5000 cells, 10,000 cells
or more. Therefore, the number of cells within a multicellular
interaction model or used in a method of predicting a behavior of
such multicellular interactions can include some or ail cells which
constitute a group of interacting cells, a tissue, organ,
physiological system or whole organism. The multicellular
interaction models and methods of the invention also can include
some or all cells which constitute a group of interacting cells of
different types or from different tissues, organs, physiological
systems or organisms. The organism can be single cell prokaryotic
or eukaryotic organism or multicellular eukaryotic organisms.
Specific examples of different cell types include a mammary gland
cell, hepatocyte, white fat cell, brown fat cell, liver lipocyte,
red skeletal muscle cell, white skeletal muscle cell, intermediate
skeletal muscle cell, smooth muscle cell, red blood cell,
adipocyte, monocyte, reticulocyte, fibroblast, neuronal cell
epithelial cell or one or more cells set forth in Table 5. Specific
examples of physiological functions resulting from multicellular
interactions that can be predicted include metabolite yield, ATP
yield, biomass demand, growth, triacylglycerol storage, muscle
contraction, milk secretion and oxygen transport capacity.
[0117] Intra-system reactions of a multicellular interaction model
or method of the invention has been exemplified above and below
with reference to the extracellular in vivo environment and, in
particular, with reference to buffering this environment by
supplying functions of the renal system. Given the teachings and
guidance provided herein, those skilled in the art will understand
that any extracellular reaction, plurality of reactions, function
of the extracellular space or function supplied into the
extracellular space by another cell, tissue or physiological system
can be employed as an intra-system reaction network. Such reactions
or activities can represent normal or pathological conditions or
both conditions occurring within this intra-system environment.
Specific examples of intra-system reactions include one or more
reactions performed in the hematopoietic system, urine, connective
tissue, contractile tissue or cells, lymphatic system, respiratory
system or renal system. Reactions or reactants included in one or
more intra-system data structures can be, for example, bicarbonate
buffer system, an ammonia buffer system, a hormone, a signaling
molecule, a vitamin, a mineral or a combination thereof.
[0118] The in silico models of multicellular or multi-network
interactions, including Homo sapiens model and methods, described
herein can be implemented on any conventional host computer system,
such as those based on Intel.TM.. microprocessors and running
Microsoft Windows operating systems. Other systems, such as those
using the UNIX or LINUX operating system and based on IBM.TM.,
DECR.TM.. or Motorola.TM.. microprocessors are also contemplated.
The systems and methods described herein can also be implemented to
run on client-server systems and wide-area networks, such as the
Internet.
[0119] Software to implement a method or model of the invention can
be written in any well-known computer language, such as Java, C,
C++, Visual Basic, FORTRAN or COBOL and compiled using any
well-known compatible compiler. The software of the invention
normally runs from instructions stored in a memory on a host
computer system. A memory or computer readable medium can be a hard
disk, floppy disc, compact disc, magneto-optical disc, Random
Access Memory, Read Only Memory or Flash Memory. The memory or
computer readable medium used in the invention can be contained
within a single computer or distributed in a network. A network can
be any of a number of conventional network systems known in the art
such as a local area network (LAN) or a wide area network (WAN).
Client-server environments, database servers and networks that can
be used in the invention are well known in the art. For example,
the database server can run on an operating system such as UNIX,
running a relational database management system, a World Wide Web
application and a World Wide Web server. Other types of memories
and computer readable media are also contemplated to function
within the scope of the invention.
[0120] A database or data structure of the invention can be
represented in a markup language format including, for example,
Standard Generalized. Markup Language (SGML), Hypertext markup
language (HTML) or Extensible Markup language (XML). Markup
languages can be used to tag the information stored in a database
or data structure of the invention, thereby providing convenient
annotation and transfer of data between databases and data
structures. In particular, an XML format can be useful for
structuring the data representation of reactions, reactants and
their annotations; for exchanging database contents, for example,
over a network or Internet: for updating individual elements using
the document object model; or for providing differential access to
multiple users for different information content of a data base or
data structure of the invention. XML programming methods and
editors for writing XML code are known in the art as described, for
example, in Ray, "Learning XML" O'Reilly and Associates,
Sebastopol, Calif. (2001).
[0121] A set of constraints can be applied to a reaction network
data structure to simulate the flux of mass through the reaction
network under a particular set of environmental conditions
specified by a constraints set. Because the time constants
characterizing metabolic transients and/or metabolic reactions are
typically very rapid, on the order of milli-seconds to seconds,
compared to the time constants of cell growth on the order of hours
to days, the transient mass balances can be simplified to only
consider the steady state behavior. Referring now to an example
where the reaction network data stucture is a stoichiometric
matrix, the steady state mass balances can be applied using the
following system of linear equations
Sv=0 (Eq. 5)
where S is the stoichiometric matrix as defined above and v is the
flux vector. This equation defines the mass, energy, and redox
potential constraints placed on the metabolic network as a result
of stoichiometry. Together Equations 1 and 5 representing the
reaction constraints and mass balances, respectively, effectively
define the capabilities and constraints of the metabolic genotype
and the organism's metabolic potential. All vectors, v, that
satisfy Equation 5 are said to occur in the mathematical nullspace
of S. Thus, the null space defines steady-state metabolic flux
distributions that do not violate the mass, energy, or redox
balance constraints. Typically, the number of fluxes is greater
than the number of mass balance constraints, thus a plurality of
flux distributions satisfy the mass balance constraints and occupy
the null space. The null space, which defines the feasible set of
metabolic flux distributions, is further reduced in size by
applying the reaction constraints set forth in Equation 1 leading
to a defined solution space. A point in this space represents a
flux distribution and hence a metabolic phenotype for the network.
An optimal solution within the set of all solutions can be
determined using mathematical optimization methods when provided
with a stated objective and a constraint set. The calculation of
any solution constitutes a simulation of the model.
[0122] Objectives for activity of a human cell can be chosen. While
the overall objective of a multi-cellular organism may be growth or
reproduction, individual human cell types generally have much more
complex objectives, even to the seemingly extreme objective of
apoptosis (programmed cell death), which may benefit the organism
but certainly not the individual cell. For example, certain cell
types may have the objective of maximizing energy production, while
others have the objective of maximizing the production of a
particular hormone, extracellular matrix component, or a mechanical
property such as contractile force. In cases where cell
reproduction is slow, such as human skeletal muscle, growth and its
effects need not be taken into account. In other cases, biomass
composition and growth rate could be incorporated into a
"maintenance" type of flux, where rather than optimizing for
growth, production of precursors is set at a level consistent with
experimental knowledge and a different objective is optimized.
[0123] Certain cell types, including cancer cells, can be viewed as
having an objective of maximizing cell growth. Growth can be
defined in terms of biosynthetic requirements based on literature
values of biomass composition or experimentally determined values
such as those obtained as described above. Thus, biomass generation
can be defined as an exchange reaction that removes intermediate
metabolites in the appropriate ratios and represented as an
objective function, In addition to draining intermediate
metabolites this reaction flux can be formed to utilize energy
molecules such as ATP, NADH and NADPH so as to incorporate any
maintenance requirement that must be met. This new reaction flux
then becomes another constraint/balance equation that the system
must satisfy as the objective function. Using the stoichiometric
matrix of FIG. 3 as an example, adding such a constraint is
analogous to adding the additional column to the stoichiometric
matrix to represent fluxes to describe the production demands
placed on the metabolic system. Setting this new flux as the
objective function and asking the system to maximize the value of
this flux for a given set of constraints on all the other fluxes is
then a method to simulate the growth of the organism.
[0124] Continuing with the example of the stoichiometric matrix
applying a constraint set to a reaction network data structure can
be illustrated as follows. The solution to equation 5 can be
formulated as an optimization problem, in which the flux
distribution that minimizes a particular objective is found.
Mathematically, this optimization problem can be stated as:
Minimize Z (Eq. 6)
where z=.SIGMA.c.sub.1v.sub.t (Eq. 7)
where Z is the objective which is represented as a linear
combination of metabolic fluxes v.sub.i using the weights c.sub.i
in this linear combination. The optimization problem can also be
stated as the equivalent maximization problem; i.e. by changing the
sign on Z. Any commands for solving the optimization problem can be
used including, for example, linear programming commands.
[0125] A computer system of the invention can further include a
user interface capable of receiving a representation of one or more
reactions. A user interface of the invention can also be capable of
sending at least one command for modifying the data structure, the
constraint set or the commands for applying the constraint set to
the data representation, or a combination thereof. The interface
can be a graphic user interface having graphical means for making
selections such as menus or dialog boxes. The interface can be
arranged with layered screens accessible by making selections from
a main screen. The user interface can provide access to other
databases useful in the invention such as a metabolic reaction
database or links to other databases having information relevant to
the reactions or reactants in the reaction network data structure
or to a multicellular organism's physiology, including Homo sapiens
physiology. Also, the user interface can display a graphical
representation of a reaction network or the results of a simulation
using a model of the invention.
[0126] Once an initial reaction network data structure and set of
constraints has been created, this model can be tested by
preliminary simulation. During preliminary simulation, gaps in the
network or "dead-ends" in which a metabolite can be produced but
not consumed or where a metabolite can be consumed but not produced
can be identified. Based on the results of preliminary simulations
areas of the metabolic reconstruction that require an additional
reaction can be identified. The determination of these gaps can be
readily calculated through appropriate queries of the reaction
network data structure and need not require the use of simulation
strategies, however, simulation would be an alternative approach to
locating such gaps.
[0127] In the preliminary simulation testing and model content
refinement stage the existing model is subjected to a series of
functional tests to determine if it can perform basic requirements
such as the ability to produce the required biomass constituents
and generate predictions concerning the basic physiological
characteristics of the particular cell type being modeled. The more
preliminary testing that is conducted the higher the quality of the
model that will be generated. Typically, the majority of the
simulations used in this stage of development will be single
optimizations. A single optimization can be used to calculate a
single flux distribution demonstrating how metabolic resources are
routed determined from the solution to one optimization problem. An
optimization problem can be solved using linear programming as
demonstrated in the Examples below. The result can be viewed as a
display of a flux distribution on a reaction map. Temporary
reactions can be added to the network to determine if they should
be included into the model based on modeling/simulation
requirements.
[0128] Once a model of the invention is sufficiently complete with
respect to the content of the reaction network data structure
according to the criteria set forth above, the model can be used to
simulate activity of one or more reactions in a reaction network.
The results of a simulation can be displayed in a variety of
formats including, for example, a table, graph, reaction network,
flux distribution map or a phenotypic phase plane graph.
[0129] Thus, the invention provides a method for predicting a Homo
sapiens physiological function. The method includes the steps of
(a) providing a data structure relating a plurality of Homo sapiens
reactants to a plurality of Homo sapiens reactions, wherein each of
the Homo sapiens reactions includes a reactant identified as a
substrate of the reaction, a reactant identified as a product of
the reaction and a stoichiometric coefficient relating said
substrate and said product; (b) providing a constraint set for the
plurality of Homo sapiens reactions; (c) providing an objective
function, and (d) determining at least one flux distribution that
minimizes or maximizes the objective function when the constraint
set is applied to the data structure, thereby predicting a Homo
sapiens physiological function.
[0130] A method for predicting a Homo sapiens physiological
function can include the steps of (a) providing a data structure
relating a plurality of Homo sapiens reactants to a plurality of
Homo sapiens reactions, wherein each of the Homo sapiens reactions
includes a reactant identified as a substrate of the reaction, a
reactant identified as a product of the reaction and a
stoichiometric coefficient relating the substrate and the product,
and wherein at least one of the reactions is a regulated reaction:
(b) providing a constraint set for the plurality of reactions,
wherein the constraint set includes a variable constraint for the
regulated reaction; (c) providing a condition-dependent value to
the variable constraint; (d) providing an objective function, and
(e) determining at least one flux distribution that minimizes or
maximizes the objective function when the constraint set is applied
to the data structure, thereby predicting a Homo sapiens
physiological function.
[0131] Further, a method for predicting a physiological function of
a multicellular organism also is provided. The method includes: (a)
providing a first data structure relating a plurality of reactants
to a plurality of reactions from a first cell, each of said
reactions comprising a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product; (b) providing a second data structure relating a plurality
of reactants to a plurality of reactions from a second cell, each
of said reactions comprising a reactant identified as a substrate
of the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating said substrate and said
product: (c) providing a third data structure relating a plurality
of intra-system reactants to a plurality of intra-system reactions
between said first and second cells, each of said intra-system
reactions comprising a reactant identified as a substrate of the
reaction, a reactant identified as a product of the reaction and a
stoichiometric coefficient relating said substrate and said
product: (d) providing a constraint set for said plurality of
reactions for said first, second and third data structures; (e)
providing an objective function, and (f) determining at least one
flux distribution that minimizes or maximizes an objective function
when said constraint set is applied to said first and second data
structures, wherein said at least one flux distribution is
predictive of a physiological function of said first and second
cells.
[0132] As used herein, the term "physiological function," when used
in reference to Homo sapiens, is intended to mean an activity of an
organism as a whole, including a multicellular organism and/or a
Homo sapiens organism or cell as a whole. An activity included in
the term can be the magnitude or rate of a change from an initial
state of, for example, two or more interacting cells or a Homo
sapiens cell to a final state of the two or more interacting cells
or the Homo sapiens cell. An activity included in the term can be,
for example, growth, energy production, redox equivalent
production, biomass production, development, or consumption of
carbon nitrogen, sulfur, phosphate, hydrogen or oxygen. An activity
can also be an output of a particular reaction that is determined
or predicted in the context of substantially all of the reactions
that affect the particular reaction in two or more interacting
cells or a Homo sapiens cell, for example, or substantially all of
the reactions that occur in a plurality of interacting cells such
as a tissue, organ or organism, or substantially all of the
reactions that occur in a Homo sapiens cell (e.g., muscle
contraction). Examples of a particular reaction included in the
term are production of biomass precursors, production of a protein,
production of an amino acid, production of a purine, production of
a pyrimidine, production of a lipid, production of a fatty acid,
production of a cofactor or transport of a metabolite. A
physiological function can include an emergent property which
emerges from the whole but not from the sum of parts where the
parts are observed in isolation (see for example, Nilsson, Nat.
Biotech 18:1147-1150 (2000)),
[0133] A physiological function of reactions within two or more
interacting cells, including Homo sapiens reactions, can be
determined using phase plane analysis of flux distributions. Phase
planes are representations of the feasible set which can be
presented in two or three dimensions. As an example, two parameters
that describe the growth conditions such as substrate and oxygen
uptake rates can be defined as two axes of a two-dimensional space.
The optimal flux distribution can be calculated from a reaction
network data structure and a set of constraints as set forth above
for all points in this plane by repeatedly solving the linear
programming problem while adjusting the exchange fluxes defining
the two-dimensional space. A finite number of qualitatively
different metabolic pathway utilization patterns can be identified
in such a plane, and lines can be drawn to demarcate these regions.
The demarcations defining the regions can be determined using
shadow prices of linear optimization as described, for example in
Chvatal, Linear Programming New York, W.H. Freeman and Co. (1983).
The regions are referred to as regions of constant shadow price
structure. The shadow prices define the intrinsic value of each
reactant toward the objective function as a number that is either
negative, zero, or positive and are graphed according to the uptake
rates represented by the x and y axes. When the shadow prices
become zero as the value of the uptake rates are changed there is a
qualitative shift in the optimal reaction network.
[0134] One demarcation line in the phenotype phase plane is defined
as the line of optimality (LO). This line represents the optimal
relation between respective metabolic fluxes. The LO can be
identified by varying the x-axis flux and calculating the optimal
y-axis flux with the objective function defined as the growth flux.
From the phenotype phase plane analysis the conditions under which
a desired activity is optimal can be determined. The maximal uptake
rates lead to the definition of a finite area of the plot that is
the predicted outcome of a reaction network within the
environmental conditions represented by the constraint set. Similar
analyses can be performed in multiple dimensions where each
dimension on the plot corresponds to a different uptake rate. These
and other methods for using phase plane analysis, such as those
described in Edwards et al., Biotech Bioeng. 77:27-36(2002), can be
used to analyze the results of a simulation using an in silica Homo
sapiens model of the invention,
[0135] A physiological function of Homo sapiens can also be
determined using a reaction map to display a flux distribution. A
reaction map of Homo sapiens can be used to view reaction networks
at a variety of levels. In the case of a cellular metabolic
reaction network a reaction map can contain the entire reaction
complement representing a global perspective. Alternatively, a
reaction map can focus on a particular region of metabolism such as
a region corresponding to a reaction subsystem described above or
even on an individual pathway or reaction.
[0136] Thus, the invention provides an apparatus that produces a
representation of a Homo sapiens physiological function, wherein
the representation is produced by a process including the steps of
(a) providing a data stricture relating a plurality of Homo sapiens
reactants to a plurality of Homo sapiens reactions, wherein each of
the Homo sapiens reactions includes a reactant identified as a
substrate of the reaction, a reactant identified as a product of
the reaction and a stoichiometric coefficient relating said
substrate and said product: (b) providing a constraint set for the
plurality of Homo sapiens reactions; (c) providing an objective
function; (d) determining at least one flux distribution that
minimizes or maximizes the objective function when the constraint
set is applied to the data structure. thereby predicting a Homo
sapiens physiological function, and (e) producing a representation
of the activity of the one or more Homo sapiens reactions.
Similarly, the invention provides an apparatus that produces a
representation of two or more interacting cells, including a
tissue, organ, physiological system or whole organism wherein data
structures are provided relating a plurality o f reactants to a
plurality of reactions for each type of interacting cell and for
one or more intra-system functions. A constraint set is provided
for the plurality of reactions for the plurality of data structures
as well as an objective function that minimizes or maximizes an
objective function when the constraint set is applied to predict a
physiological function of the two or more interacting cells. The
apparatus produces a representation of the activity of one more
reactions of the two or more interacting cells.
[0137] The methods of the invention can be used to determine the
activity of a plurality of Homo sapiens reactions including, for
example, biosynthesis of an amino acid, degradation of an amino
acid, biosynthesis of a purine, biosynthesis of a pyrimidine,
biosynthesis of a lipid, metabolism of a fatty acid, biosynthesis
of a cofactor, transport of a metabolite and metabolism of an
alternative carbon source. In addition, the methods can be used to
determine the activity of one or more of the reactions described
above or listed in Table 1.
[0138] The methods of the invention can be used to determine a
phenotype of a Homo sapiens mutant or aberrant cellular interaction
between two or more cells. The activity alone or more reactions can
be determined using the methods described above, wherein the
reaction network data structure lacks one or more gene-associated
reactions that occur in Homo sapiens or in a multicellular organism
or multicellular interaction. Alternatively, the methods can be
used to determine the activity of one or more reactions when a
reaction that does not naturally occur in the model of
multicellular interactions or in Homo sapiens, for example, is
added to the reaction network data structure. Deletion of a gene
can also be represented in a model of the invention by constraining
the flux through the reaction to zero, thereby allowing the
reaction to remain within the data structure. Thus, simulations can
be made to predict the effects of adding or removing genes to or
from one or more cells within a multicellular interaction,
including Homo sapiens and/or a Homo sapiens cell. The methods can
be particularly useful for determining the effects of adding or
deleting a gene that encodes for a gene product that performs a
reaction in a peripheral metabolic pathway.
[0139] A drug target or target for any other agent that affects a
function of a multicellular interaction, including a Homo sapiens
function can be predicted using the methods of the invention. Such
predictions can be made by removing a reaction to simulate total
inhibition or prevention by a drug or agent. Alternatively, partial
inhibition or reduction in the activity a particular reaction can
be predicted by performing the methods with altered constraints.
For example, reduced activity can be introduced into a model of the
invention by altering the a.sub.j or b.sub.j values for the
metabolic flux vector of a target reaction to reflect a finite
maximum or minimum flux value corresponding to the level of
inhibition. Similarly, the effects of activating a reaction, by
initiating or increasing the activity of the reaction, can be
predicted by performing the methods with a reaction network data
structure lacking a particular reaction or by altering the a.sub.j
or b.sub.j values for the metabolic flux vector of a target
reaction to reflect a maximum or minimum flux value corresponding
to the level of activation. The methods can be particularly useful
for identifying a target in a peripheral metabolic pathway.
[0140] Once a reaction has been identified for which activation or
inhibition produces a desired effect on a function of a
multicellular interaction, including a Homo sapiens function, an
enzyme or macromolecule that performs the reaction in the
multicellular system or a gene that expresses the enzyme or
macromolecule can be identified as a target for a drug or other
agent. A candidate compound for a target identified by the methods
of the invention can be isolated or synthesized using known
methods. Such methods for isolating or synthesizing compounds can
include, for example, rational design based on known properties of
the target (see, for example, DeCamp et al., Protein Engineering
Principles and Practice, Ed. Cleland and Craik, Wiley-Liss, New
York, pp. 467-506 (1996)), screening the target against
combinatorial libraries of compounds (see for example. Houghten et
al., Nature, 354, 84-86 (1991): Dooley et al., Science 266,
2019-2022 (1994), which describe an iterative approach, or R.
Houghten et al. PCT/US91/08694 and U.S. Pat. No. 5,556,762 which
describe the positional-scanning approach), or a combination of
both to obtain focused libraries. Those skilled in the art will
know or will be able to routinely determine assay conditions to be
used in a screen based on properties of the target or activity
assays known in the art.
[0141] A candidate drug or agent, whether identified by the methods
described above or by other methods known in the art, can be
validated using an in silico model or method of multicellular
interactions, including a Homo sapiens model or method of the
invention. The effect of a candidate drug or agent on physiological
function can be predicted based on the activity for a target in the
presence of the candidate drug or agent measured in vitro or in
vivo. This activity can be represented in an in silica model of the
multicellular system by adding a reaction to the model, removing a
reaction from the model or adjusting a constraint for a reaction in
the model to reflect the measured effect of the candidate drug or
agent on the activity of the reaction. By running a simulation
under these conditions the holistic effect of the candidate drug or
agent on the physiological function of the multicellular system,
including Homo sapiens physiological function can be predicted.
[0142] The methods of the invention can be used to determine the
effects of one or more environmental components or conditions on an
activity of, for example, a multicellular interaction, a tissue,
organ. physiological function or a Homo sapiens cell. As set forth
above an exchange reaction can be added to a reaction network data
structure corresponding to uptake of an environmental component,
release of a component to the environment, or other environmental
demand. The effect of the environmental component or condition can
be further investigated by running simulations with adjusted
a.sub.j or b.sub.j values for the metabolic flux vector of the
exchange reaction target reaction to reflect a finite maximum or
minimum flux value corresponding to the effect of the environmental
component or condition. The environmental component can be, for
example an alternative carbon source or a metabolite that when
added to the environment of a multicellular system. organism or
Homo sapiens cell can be taken up and metabolized. The
environmental component can also be a combination of components
present for example in a minimal medium composition. Thus, the
methods can be used to determine an optimal or minimal medium
composition that is capable of supporting a particular activity of
a multicellular interaction or system, including a particular
activity of Homo sapiens.
[0143] The invention further provides a method for determining a
set of environmental components to achieve a desired activity for
Homo sapiens. The method includes the steps of (a) providing a data
structure relating a plurality of Homo sapiens reactants to a
plurality of Homo sapiens reactions, wherein each of the Homo
sapiens reactions includes a reactant identified as a substrate of
the reaction, a reactant identified as a product of the reaction
and a stoichiometric coefficient relating the substrate and the
product; (b)providing a constraint set for the plurality of Homo
sapiens reactions; (c) applying the constraint set to the data
representation, thereby determining the activity of one or more
Homo sapiens reactions (d) determining the activity of one or more
Homo sapiens reactions according to steps (a) through (c), wherein
the constraint set includes an upper or lower bound on the amount
of an environmental component and (e) repeating steps (a) through
(c) with a changed constraint set, wherein the activity determined
in step (e) is improved compared to the activity determined in step
(d). Similarly. a method for determining a set of environmental
components to achieve a desired activity for a multicellular
interaction also is provided. The method includes providing a
plurality of data structures relating a plurality of reactants to a
plurality of reactions for each type of interacting cell and for
one or more intra-system functions; providing a constraint set for
the plurality of reactions for the plurality of data structures as
well as providing an objective function that minimizes or maximizes
an objective function when the constraint set is applied to predict
a physiological function of the two or more interacting cells;
determining the activity of one or more reactions within two or
more interacting cells using a constraint set having an upper or
lower bound on the amount of an environmental component and
repeating these steps until the activity is improved.
[0144] It is understood that modifications which do not
substantially affect the activity of the various embodiments of
this invention are also included within the definition of the
invention provided herein. Accordingly, the following examples are
intended to illustrate but not limit the present invention.
EXAMPLE I
[0145] This example shows the construction of a universal Homo
sapiens metabolic reaction database, a Homo sapiens core metabolic
reaction database and a Homo sapiens muscle cell metabolic reaction
database. This example also shows the iterative model building
process used to generate a Homo sapiens core metabolic model and a
Homo sapiens muscle cell metabolic model.
[0146] A universal Homo sapiens reaction database was prepared from
the genome databases and biochemical literature. The reaction
database shown in Table 1 contains the following information:
[0147] Locus ID--the locus number of the gene found in the
LocusLink website. [0148] Gene Ab.--various abbreviations which are
used for the gene. [0149] Reaction Stoichiometry--includes all
metabolites and direction of the reaction, as well as
reversibility, [0150] E.C.--The Enzyme Commission number.
[0151] Additional information included in the universal reaction
database, although not shown in Table 1, included the chapter of
Salway, supra (1999), where relevant reactions were found; the
cellular location, if the reaction primarily occurs in a given
compartment the SWISS PROT identifier, which can be used to locate
the gene record in SWISS PROT; the full name of the gene at the
given locus; the chromosomal location of the gene; the Mendelian
Inheritance in Man (MIM) data associated with the gene; and the
tissue type, if the gene is primarily expressed in a certain
tissue. Overall, 1130 metabolic enzyme- or transporter-encoding
genes were included in the universal reaction database.
[0152] Fifty-nine reactions in the universal reaction database were
identified and included based on biological data as found in Salway
supra (1999), currently without genome annotation. Ten additional
reactions, not described in the biochemical literature or genome
annotation, were subsequently included in the reaction database
following preliminary simulation testing and model content
refinement. These 69 reactions are shown at the end of Table 1.
[0153] From the universal Homo sapiens reaction database shown in
Table 1, a core metabolic reaction database was established, which
included core metabolic reactions as well as some amino acid and
fatty acid metabolic reactions, as described in Chapters 1, 3, 4,
7, 9, 10. 13, 17, 18 and 44 of J. G. Salway, Metabolism at a
Glance, 2.sup.nd ed., Blackwell Science, Malden, Mass. (1999). The
core metabolic reaction database included 211 unique reactions,
accounting for 737 genes in the Homo sapiens genome. The core
metabolic reaction database was used, although not in its entirety,
to create the core metabolic model described in Example II.
[0154] To allow for the modeling of muscle cells, the core reaction
database was expanded to include 446 unique reactions, accounting
for 889 genes in the Homo sapiens genome. This skeletal muscle
metabolic reaction database was used to create the skeletal muscle
metabolic model described in Example II.
[0155] Once the core and muscle cell metabolic reaction databases
were compiled, the reactions were represented as a metabolic
network data structure, or "stoichiometric input file." For
example, the core metabolic network data structure shown in Table 2
contains 33 reversible reactions. 31 non-reversible reactions, 97
matrix columns and 52 unique enzymes. Each reaction in Table 2 is
represented so as to indicate the substrate or substrates (a
negative number) and the product or products (a positive number);
the stoichiometry; the name of each reaction (the term following
the zero): and whether the reaction is reversible (an R following
the reaction name). A metabolite that appears in the mitochondria
is indicated by an "m," and a metabolite that appears in the
extracellular space is indicated by an "ex."
[0156] To perform a preliminary simulation or to simulate a
physiological condition, a set of inputs and outputs has to be
defined and the network objective function specified. To calculate
the maximum ATP production of the Homo sapiens core metabolic
network using glucose as a carbon source, a non-zero uptake value
for glucose was assigned and ATP production was maximized as the
objective function, using the representation shown in Table 2. The
network's performance was examined by optimizing for the given
objective function and the set of constraints defined in the input
file, using flux balance analysis methods. The model was refined in
an iterative manner by examining the results of the simulation and
implementing the appropriate changes.
[0157] Using this iterative procedure, two metabolic reaction
networks were generated, representing human core metabolism and
human skeletal muscle cell metabolism.
EXAMPLE II
[0158] This example shows how human metabolism can be accurately
simulated using a Homo sapiens core metabolic model.
[0159] The human core metabolic reaction database shown in Table 3
was used in simulations of human core metabolism. This reaction
database contains a total of 65 reactions, covering the classic
biochemical pathways of glycolysis, the pentose phosphate pathway,
the tricitric acid cycle, oxidative phosphorylation, glycogen
storage, the malate/aspartate shuttle, the glycerol phosphate
shuttle, and plasma and mitochondrial membrane transporters. The
reaction network was divided into three compartments: the cytosol,
mitochondria, and the extracellular space. The total number of
metabolites in the network is 50, of which 35 also appear in the
mitochondria, This core metabolic network accounts for 250 human
genes.
[0160] To perform simulations using the core metabolic network,
network properties such as the P/O ratio were specified using
Salway. supra (1999) as a reference, Oxidation of NADH through the
Electron Transport System (ETS) was set to generate 2.5 ATP
molecules (i.e. a P/O ratio of 2.5 for NADH), and that of
FADH.sub.2 was set to 1.5 ATP molecules (i.e. a P/O ratio of 1.5
for FADH.sub.2).
[0161] Using the core metabolic network, aerobic and anaerobic
metabolisms were simulated in silico. Secretion of metabolic
by-products was in agreement with the known physiological
parameters. Maximum yield of all 12 precursor-metabolites
(glucose-6-phosphate, fructose-6-phosphate, ribose-5-phosphate,
erythrose-4-phosphate, triose phosphate, 3-phosphoglycerate,
phosphoenolpyruvate, pyruvate, acetyl CoA, .alpha.-ketoglutarate,
succinyl CoA, and oxaloacetate) was examined and none found to
exceed the values of its theoretical yield.
[0162] Maximum ATP yield was also examined in the cytosol and
mitochondria. Salway, supra (1999) reports that in the absence of
membrane proton-coupled transport systems, the energy yield is 38
ATP molecules per molecule of glucose and otherwise 31 ATP
molecules per molecule of glucose. The core metabolic model
demonstrated the same values as described by Salway supra (1999).
Energy yield in the mitochondria was determined to be 38 molecules
of ATP per glucose molecule. This is equivalent to production of
energy in the absence of proton-couple transporters across
mitochondrial membrane since all the protons were utilized only in
oxidative phosphorylation. In the cytosol, energy yield was
calculated to be 30.5 molecules of .ATP per glucose molecule. This
value reflects the cost of metabolite exchange across the
mitochondrial membrane as described by Salway, supra (1999).
EXAMPLE III
[0163] This example shows how human muscle cell metabolism can be
accurately simulated under various physiological and pathological
conditions using a Homo sapiens muscle cell metabolic model.
[0164] As described in Example I, the core metabolic model was
extended to also include all the major reactions occurring in the
skeletal muscle cell, adding new functions to the classical
metabolic pathways found in the core model, such as fatty acid
synthesis and .beta.-oxidation, triacylglycerol and phospholipid
formation, and amino acid metabolism. Simulations were performed
using the muscle cell reaction database shown in Table 4. The
biochemical reactions were again compartmentalized into cytosolic
and mitochondrial compartments.
[0165] To simulate physiological behavior of human skeletal muscle
cells, an objective function had to be defined. Growth of muscle
cells occurs in time scales of several hours to days. The time
scale of interest in the simulation, however, was in the order of
several to tens of minutes, reflecting the time period of metabolic
changes during exercise. Thus, contraction (defined as, and related
to energy production) was chosen to be the objective function, and
no additional constraints were imposed to represent growth demands
in the cell.
[0166] To study and test the behavior of the network, twelve
physiological cases (Table 8) and five disease cases (Table 9) were
examined. The input and output of metabolites were specified as
indicated in Table 8, and maximum energy production and metabolite
secretions were calculated and taken into account.
TABLE-US-00001 TABLE 8 Metabolite Exchange 1 2 3 4 5 6 7 8 9 10 11
12 Glucose I I -- -- I I -- -- -- -- -- -- O2 I -- I -- I -- I -- I
-- I -- Palmitate I I -- -- -- -- -- -- I I -- -- Glycogen I I I I
-- -- -- -- -- -- -- -- Phospho- I I -- -- -- -- -- -- -- -- I I
creatine Triacylglycerol I I -- -- -- -- I I -- -- -- -- Isoleucine
I I -- -- -- -- -- -- -- -- -- -- Valine I I -- -- -- -- -- -- --
-- -- -- Hydroxy- -- -- -- -- -- -- -- -- -- -- -- -- butyrate
Pyruvate O O O O O O O O O O O O Lactate O O O O O O O O O O O O
Albumin O O O O O O O O O O O O
TABLE-US-00002 TABLE 9 Reaction Disease Enzyme Deficiency
Constrained McArdle's disease phosphorylase GBE1 Tarui's disease
phosphofructokianse PFKL Phosphoglycerate kinase phosphoglycerate
kinase PGK1R deficiency Phosphoglycerate mutase phosphoglycerate
mutase PGAM3R deficiency Lactate dehydrogenase Lactate dehyrogenase
LDHAR deficiency
[0167] The skeletal muscle model was tested for utilization of
various carbon sources available during various stages of exercise
and food starvation (Table 8). The by-product secretion of the
network in an aerobic to anaerobic shift was qualitatively compared
to physiological outcome of exercise and found to exhibit the same
general features such as secretion of fermentative by-products and
lowered energy yield.
[0168] The network behavior was also examined for five disease
cases (Table 9). The test cases were chosen based on their
physiological relevance to the model's predictive capabilities. In
brief, McArdle's disease is marked by the impairment of glycogen
breakdown. Tarui's disease is characterized by a deficiency in
phosphofructokinase. The remaining diseases examined are marked by
a deficiency of metabolic enzymes phosphoglycerate kinase,
phosphoglycerate mutase, and lactate dehydrogenase. In each case,
the changes in flux and by-product secretion of metabolites were
examined for an aerobic to anaerobic metabolic shift with glycogen
and phosphocreatine as the sole carbon sources to the network and
pyruvate, lactate. and albumin as the only metabolic by-products
allowed to leave the system. To simulate the disease cases, the
corresponding deficient enzyme was constrained to zero. In all
cases, a severe reduction in energy production was demonstrated
during exercise, representing the state of the disease as seen in
clinical cases.
EXAMPLE IV
[0169] This Example shows the construction and simulation of a
multi-cellular model demonstrating the interactions between human
adipocytes and monocytes.
[0170] The specific examples described above demonstrate the use a
constraint-based approach in modeling metabolism in microbial
organisms including prokaryotes such as E. coli and eukaryotes such
as S. Cerevisiae as well as for complex multicellular organisms
requiring regulatory interactions such as humans. Described below
is the modeling procedure, network content, and simulation results
including network characteristics and metabolic performance of an
integrated two-cell model of human adipocyte (fatty cell) and
myocyte (muscle cell) using the compositions and methods of the
invention. Simulations were performed to exemplify the coupled
function of the two cell types during distinct physiological
conditions corresponding to the coupled function of adipocyes and
myocytes during sprint and marathon physiological conditions.
[0171] A human metabolic network model was reconstructed using
biochemical, physiological, and genomic data as described
previously. Briefly, the central metabolic network was used as a
template for the construction of cell-specific models by adding
biochemical reactions known to occur in specific cell-types of
interest based on genomic, biochemical, and/or physiological
information. Other methods for reconstructing the cell-specific
models included reconstructing all the biochemical pathways and
biochemical reactions that occur in the human metabolism regardless
of their tissue specificity and location within the cell in a
database and then reconstructing cell-, tissue-, organ-specific
models by separating reactions that occur in specified cells,
tissues, and/or organs based on genomic, physiological,
biochemical, and/or high throughput data such as gene expression.
proteomics, metabolomics, and other types of "omic" data. In this
latter approach, in addition to the cell-, tissue-, and/or
organ-specific reactions, reactions can be added to balance
metabolites and represent the biochemistry, physiology, and
genetics of the cells, tissues, organs, and/or whole human body. In
the approach described below, the initial reconstruction of a
central metabolic network followed by development of cell-specific
models, the reconstruction of a generic central metabolic network
is not a necessary step in reconstructing and modeling human
metabolism. Rather, it is performed to accelerate the
reconstruction process.
[0172] implementation of the multi-cellular adipocyte-myocyte model
is described below with reference to the reconstruction of the
constituent components. In this regard, the reconstruction of a
central human metabolic network is described first followed by the
reconstruction procedures for fatty cell and muscle cell specific
networks. The reconstruction procedure by which the two
cell-specific models were combined to generate a multi-cellular
model for human metabolism is then described.
Metabolic Network of Central Human Metabolism
[0173] The metabolic network of the central human metabolism was
constructed as a template and a starting point for reconstructing
more specific cell models. To construct a central metabolic network
for human metabolism, a compendium of 1557 annotated human genes
obtained front Kyoto Encyclopedia of Genes and Genomes KEGG,
National Center for Biotechnology Information or NCBI, and the
Universal Protein Resource or UniProt databases was used. In
addition to the genomic and proteomic data, several primary
textbooks and publications on the biochemistry of human metabolism
also were used and included the Human Metabolism: Functional
Diversity and Integration, Ed. by J. R. Bronk, Harlow, Addison,
Wesley, Longman (1999); Textbook of Biochemistry with Clinical
Correlations, Ed. by Thomas M. Devlin, New York, Wiley-Liss (2002),
and Metabolism at a Glance, Ed. by J. G. Salway, Oxford, Maiden,
Mass., Blackwell Science (1999). The network reconstruction of
human central metabolism included metabolic pathways for
glycolysis, glucuneugenesis, citrate cycle (TCA cycle), pentose
phosphate pathway, galactose, malonyl-CoA, lactate, and pyruvate
metabolism. The methods described previously were similarly used
for this reconstruction as well as those described below. Metabolic
reactions were compartmentalized into extra-cellular space,
cytosol, mitochondrion, and endoplasmic reticulum. In addition to
the biochemical pathways, exchange reactions were included based on
biochemical literature and physiological evidence to provide the
transport of metabolites across different organelles and cytosolic
membrane.
[0174] The completed central metabolic network for human metabolism
is shown in FIGS. 5-1 through 5-9 where dashed lines indicate
organelle. cell, or system boundary. The large dashed rectangle
(black) represents the cytosolic membrane. The large dashed circle
(red) represents the mitochondrial membrane and small dashed circle
(green) represents the endoplasmic reticulum membrane. The human
central metabolic network contains 80 reactions of which 25 are
transporters and 60 unique metabolites 5. A representative example
of a gene-protein-reaction association is shown in FIG. 6 where the
open reading frame or ORF (7167) is associated to an mRNA
transcript (TPI1). The transcript is then associated to a
translated protein (Tpi1) that catalyzes a corresponding reaction
(TPI).
Adipocyte Metabolic Network
[0175] Adipocytes are specialized cells for synthesizing and
storing triacylglycerol. Triacylglycerols (TAG's) are synthesized
from dihydroxyacetone phosphate and fatty acids in white adipose
tissue. Triacylglycerol synthesized in adipocytes can be hydrolyzed
(or degraded) into fatty acids and glycerol via specialized
pathways in the fat cells. The fatty acids that are released from
triacylglycerol leave the cell and are transported to other cell
types such as myocytes for energy production. The fatty acid
composition of triacylglycerol in human mammary adipose tissue has
been experimentally measured (Raclot et al., 324:911-5 (1997)) and
includes essential, non-essential, saturated, unsaturated, even-,
and odd-chain fatty acids (Table 10).
TABLE-US-00003 TABLE 10 Fatty acid composition of fat cell TAG in
human, NEFA released by these cells in vitro, and relative
mobilization (% NEFA/% TAG) of fatty acids. TAG NEFA Relative Fatty
acid (weight %) (weight %) mobilization C.sub.12:0 0.50 .+-. 0.07
0.45 .+-. 0.06 0.88 .+-. 0.02 C.sub.14:0 3.08 .+-. 0.13 2.94 .+-.
0.15 0.94 .+-. 0.01 C.sub.14:1, n-7 0.03 .+-. 0.00 0.03 .+-. 0.00
1.07 .+-. 0.14 C.sub.14:1, n-5 0.20 .+-. 0.01 0.19 .+-. 0.02 0.96
.+-. 0.03 C.sub.15:0 0.33 .+-. 0.02 0.35 .+-. 0.02 1.05 .+-. 0.02
C.sub.16:0 22.79 .+-. 0.56 23.51 .+-. 0.74 1.02 .+-. 0.01
C.sub.16:1, n-9 0.54 .+-. 0.01 0.42 .+-. 0.02*** 0.77 .+-. 0.01
C.sub.16:1, n-7 2.77 .+-. 0.21 3.69 .+-. 0.34* 1.31 .+-. 0.02
C.sub.17:1, n-8 0.29 .+-. 0.02 0.36 .+-. 0.02* 1.21 .+-. 0.03
C.sub.18:0 6.67 .+-. 0.35 6.41 .+-. 1.39 0.95 .+-. 0.06 C.sub.18:1,
n-9 40.79 .+-. 0.52 39.77 .+-. 0.57 0.96 .+-. 0.01 C.sub.18:1, n-7
1.90 .+-. 0.05 2.12 .+-. 0.10 1.10 .+-. 0.03 C.sub.18:1, n-5 0.27
.+-. 0.01 0.31 .+-. 0.03 1.12 .+-. 0.04 C.sub.18:2, n-6 16.23 .+-.
0.86 16.21 .+-. 0.62 0.99 .+-. 0.01 C.sub.18:3, n-6 0.04 .+-. 0.00
0.05 .+-. 0.01 1.27 .+-. 0.07 C.sub.18:3, n-3 0.51 .+-. 0.02 0.75
.+-. 0.03*** 1.43 .+-. 0.03 C.sub.20:0 0.21 .+-. 0.02 0.10 .+-.
0.01*** 0.47 .+-. 0.04 C.sub.20:1, n-11 0.17 .+-. 0.01 0.11 .+-.
0.01*** 0.66 .+-. 0.03 C.sub.20:1, n-9 0.84 .+-. 0.02 0.53 .+-.
0.02*** 0.62 .+-. 0.01 C.sub.20:1, n-7 0.03 .+-. 0.00 0.02 .+-.
0.00* 0.67 .+-. 0.03 C.sub.20:2, n-9 0.04 .+-. 0.00 0.02 .+-.
0.00** 0.63 .+-. 0.06 C.sub.20:2, n-6 0.31 .+-. 0.02 0.26 .+-.
0.01* 0.82 .+-. 0.04 C.sub.20:3, n-6 0.26 .+-. 0.03 0.24 .+-. 0.03
0.90 .+-. 0.05 C.sub.20:3, n-3 0.03 .+-. 0.00 0.03 .+-. 0.00 0.90
.+-. 0.06 C.sub.20:4, n-6 0.35 .+-. 0.03 0.57 .+-. 0.04*** 1.60
.+-. 0.04 C.sub.20:4, n-3 0.03 .+-. 0.01 0.04 .+-. 0.01 1.13 .+-.
0.16 C.sub.20:5, n-3 0.04 .+-. 0.01 0.10 .+-. 0.01*** 2.25 .+-.
0.08 C.sub.22:0 0.04 .+-. 0.01 0.02 .+-. 0.01* 0.42 .+-. 0.05
C.sub.22:1, n-11 0.03 .+-. 0.01 0.01 .+-. 0.00* 0.37 .+-. 0.02
C.sub.22:1, n-9 0.07 .+-. 0.01 0.03 .+-. 0.00** 0.45 .+-. 0.03
C.sub.22:4, n-6 0.17 .+-. 0.02 0.10 .+-. 0.01** 0.58 .+-. 0.03
C.sub.22:5, n-6 0.02 .+-. 0.01 0.01 .+-. 0.00 0.59 .+-. 0.05
C.sub.22:5, n-3 0.20 .+-. 0.03 0.11 .+-. 0.01** 0.55 .+-. 0.02
C.sub.22:6, n-3 0.21 .+-. 0.04 0.14 .+-. 0.02* 0.65 .+-. 0.04 *P
< 0.05; **P < 0.01; ***P < 0.001
[0176] The adipocyte metabolic model was constructed by adding the
non-essential saturated, unsaturated, even- and odd-chain fatty
acid biosynthetic pathways to the central metabolic network for 21
of the fatty acids listed in Table 10. The remaining 13 essential
fatty acids were supplied to the cell via the extra-cellular space,
representing the nutritional intake from the environment. Pathway
for biosynthesis of triacylglycerol (TAG) from all 34 fatty acids
was included to account for the formation and storage of TAG in
adipocytes. Reactions for hydrolysis of TAG into fatty acids were
also included to represent TAG degradation. In addition to fatty
acid synthesis and TAG biosynthesis and degradation, transport
reactions were included to allow for the release of fatty acids
from intra-cellular space to the environment.
[0177] The metabolic model of an adipocyte cell contains a total of
198 reactions of which 63 are transporters. The adipocyte cell
model is shown in FIGS. 7-1 through 7-35 where dashed lines
indicate organelle, cell, or system boundary. The large dashed
rectangle (yellow) represents the adipocyte cytosolic membrane. The
two large dashed circles (red) represent the mitochondrial membrane
and the small dashed circle at the top (green) represents the
endoplasmic reticulum membrane. As shown, metabolic reactions were
compartmentalized into extra-cellular, cytosolic, mitochondrial,
and endoplasmic reticulum. As described above, the extra-cellular
space represents the environment outside the cell, which can
include the space outside the body, connective tissues, and
interstitial space between cells.
Myocyte Metabolic Network
[0178] The energy required for muscle contraction is generally
supplied by glucose, stored glycogen, phosphocreatine, and fatty
acids. The myocyte model was constructed by adding phosphocreatine
kinase reaction, myosin-actin activation mechanism, and
.beta.-oxidation pathway to the central metabolic network. Muscle
contraction was represented by a sequential conversion of myoactin
to myosin-ATP, myosin-ATP to myosin-ADP-P, myosin-ADP-P to
myosin-actin-ADP-P complex, myosin-actin-ADP-P to myoactin, and
subsequently the formation of muscle contraction as shown in FIG.
8.
[0179] The conversion of myoactin to myosin-actin-ADP-P complex and
muscle contraction results in a net conversion of ATP and H.sub.2O
to ADP, H', and P.
[0180] The complete reconstructed metabolic model for myocyte cell
metabolism is shown in FIGS. 9-1 through 9-35 where dashed lines
indicate organelle, cell, or system boundary. The large dashed
rectangle (brown) represents the myocyte cytosolic membrane. The
two large dashed circles (red) represent the mitochondrial
membrane. The medium sized dashed circle (purple) represents the
peroxisomal membrane and the small dashed circle (green) represents
the endoplasmic reticulum membrane. The myocyte network contains a
total of 205 reactions of which 46 are transport reactions.
Reactions for utilizing phosphcreatine as well as selected pathways
for .beta.-oxidation of saturated, unsaturated, even- and odd-chain
fatty acids and their intermediates were also included in the model
and are shown in FIGS. 9-1 through 9-35. As with the previous
network models, metabolic reactions were compartmentalized into
extra-cellular, cytosolic, mitochondrial, peroxisomal, and
endoplasmic reticulum.
Multi-Cellular Adipocyte-Myocyte Reconstruction
[0181] To generate a multi-cellular model for human metabolism, the
metabolic function of the two models of adipocyte and myocyte were
integrated by reconstructing a model that includes all the
metabolic reactions in the two individual cell types. The
interaction of the two cell types were then represented within an
"intra-system" space, which represents the connective tissues such
as blood, urine, and interstitial space, and an outside environment
or "extra-system" space. To represent the uptake of metabolites and
essential fatty acids from the environment, appropriate transport
reactions were added to exchange metabolites across the
extra-system boundary. Additional reactions also were added to
balance metabolites in the intra-system space by including the
bicarbonate and ammonia buffer systems as they function in the
kidneys. These reactions were initially omitted but were added to
improve the model once the requirement for the integrated system to
buffer extracellular protons in the interstitial space became
apparent once simulation testing began. The combined
adipocyte-myocyte model contains 430 reactions and 240 unique
metabolites. The complete reconstruction is shown in FIGS. 10-1
through 10-70 and a summary of the reactions is set forth in Table
I I. A substantially complete listing of all the reactions set
forth in FIGS. 10-1 through 10-70 is set forth below in Table
15.
TABLE-US-00004 TABLE 11 Network properties of central metabolic
network, adipocyte, myocyte, and multi-cell adipocyte-myocyte
models. Model Reactions Transporters Compounds Central Metabolism
80 25 60 Adipocyte 198 63 150 Myocyte 205 46 167 Adipocyte-Myocyte
430 135 240
[0182] In FIGS. 10-1 through 10-70, dashed lines again indicate
organelle, cell, or system boundaries. The outer most large dashed
rectangle (black) separates the environment inside and outside the
human body. The two interior dashed rectangles represents the
adipocyte cytosolic membrane (top, yellow) and the myocyte
cytosolic membrane (bottom, brown). The pair of larger dashed
circles within the adipocyte and myocyte cytosol (red) represent
the mitochondrial membrane. The medium sized dashed circle in the
myocyte cytosol (purple) represents the peroxisomal membrane and
small dashed circle within the adipocyte and myocyte cytosol
(green) represent the endoplasmic reticulum membrane.
Metabolic Simulations
[0183] The computational and infrastructure requirements for
producing the integrated multi-cellular model were assessed by
examining the network properties of first the cell-specific models,
and then the integrated multi-cellular reconstruction.
Metabolic Model of Central Human Metabolism
[0184] The metabolic capabilities of the central human model was
determined through computation of maximum yield of the 12 precursor
metabolites per glucose. The results are shown in Table 12. In all
cases, the network's yield was less or equal to the maximum
theoretical values except for succinyl-CoA. In the case of
succinyl-CoA, a higher yield was possible by incorporating CO.sub.2
via pyruvate carboxylase reaction, PCm. In addition to precursor
metabolite yields, the maximum ATP yield per mole of glucose was
computed in the network. The maximum ATP yield for the central
human metabolism was computed to be 31.5 mol ATP mol glucose, which
is consistent with previously calculated values (Vo et al., J.
Biol. Chem. 279:39532-40. (2004)).
TABLE-US-00005 TABLE 12 Maximum theoretical and central human
metabolic network yields for the precursor metabolites per glucose.
Units are in mol/mol glucose. Precursor Metabolites Theoretical
Central Metabolism Glucose 6-P 1 0.94 Fructose 6-P 1 0.94 Ribose
5-P 1.2 1.115 Erythrose 4-P 1.5 1.37 Glyceraldehyde 3-P 2 1.775 3-P
Glycerate 2 2 Phosphoenolpyruvate 2 2 Pyruvate 2 2 Oxaloacetate,
mitochondrial 2 1.969 Acetyl-CoA, mitochondrial 2 2
aKeto-glutarate, mitochondrial 1 1 Succinyl-CoA, mitochondrial 1
1.595
[0185] The biomass demand in living cells is a requirement for the
production of biosynthetic components such as amino acids, lipids
and other molecules that are needed to provide cell integrity,
maintenance, and growth. All the biosynthetic components were made
from the 12 precursor metabolites in the central metabolism shown
in Table 12. The rate of growth and biomass maintenance in
mammalian cells however is typically much lower than the rate of
metabolic activities. Thus to represent the cells' biosynthetic
requirement, a small flux demand was imposed for the production of
the 12 precursor metabolites while maximizing for ATP. In the
absence of experimental measurements, the capability of the network
to meet the biosynthetic requirements was examined by constructing
a reaction in which all the precursor metabolites were made
simultaneously with stoichiometric coefficients of one as set forth
in the reaction below:
Precursor Demand:
3pg[c]+accoa[m]+akg[m]+e4p[c]+f6p[c]+g3p[c]+g6p[c]oaa[m]+pep[c]+pyr[c]+r5-
p[c]+succoa[m].fwdarw.(2) coa[m]
[0186] In the absence of quantitative measurement, the above
reaction serves to demonstrate the ability of the network to meet
both biomass and energy requirements in the cell simultaneously.
The maximum ATP yield for the central metabolism with a demand of
0.01 mmol/gDW of precursor metabolites was computed to be 29.0,
demonstrating that the energy and carbon requirements for precursor
metabolite generation, as expected, reduce the maximum energy
production in the cell and this amount can be quantified using the
reconstructed model.
Triacylglycerol Storage and Utilization in Adipocyte Tissue
[0187] As described previously, a main function of adipocyte is to
synthesize, store, and hydrolyze triacylglycerols. The stored TAG
can be used to generate ATP during starvation or under high-energy
demand conditions. TAG hydrolysis results in the formation of fatty
acids and glycerol in adipocyte. Fatty acids are transported to
other tissues such as the muscle tissue where they can be utilized
to generate energy. Glycerol is utilized further by the liver and
other tissues where it is converted into glycerol phosphate and
enters glycolytic pathway.
[0188] To simulate the storage of triacylglycerol from glucose in
adipocyte, TAG synthesis was simulated by maximizing an internal
demand for cytosolic triacylglycerol. The maximum yield of
triacylglycerol per glucose was computed to be 0.06 mol TAG/mol
glucose, without any biomass demand. To demonstrate how the stored
TAG can he reutilized to produce fatty acids, the influx of all
other carbon sources including glucose was constrained to zero and
glycerol secretion, which is assumed to be taken up by the liver,
was maximized. When 2 mol of cytosolic proton was allowed to leave
the system, a glycerol yield of 1 mol glycerol/mol TAG or 100% was
computed. The excess two protons were formed in TAG degradation
pathway. As shown in FIG. 11, degradation of TAG was performed in
the following three steps: (1) TR1GH_ac_HS_ub; (2) 12DGRH_ac_HS_ub,
and (3) MGLYCH_ac_HS_ub). Glycerol generated as an end product of
this pathway was transported out of the cell via a proton-coupled
symport mechanism. TAG was hydrolyzed completely to fatty acids and
glycerol in three steps and in each step one proton is released.
Glycerol transport was coupled to one proton. Thus, a net amount of
two protons were generated per mol TAG degraded.
[0189] To balance protons, an ATPase reaction across the cytosolic
membrane was used. However, since the .beta.-oxidative pathways
were not included in this adipocyte model, this network is unable
to use membrane bound ATPase to balance the internal protons. When
oxidative pathways are added to the adipocyte model, the model can
completely balance protons.
[0190] In addition to triacylglycerol synthesis and hydrolysis, the
maximum ATP yield on glucose (YATP/glucose) was computed in the
adipocyte model. As for the central human metabolic network,
YATP/glucose was 31.5 mol ATP/mol glucose.
Muscle Contraction During Aerobic and Anaerobic Exercise
[0191] The required energy in muscle tissue is generally supplied
by glucose, stored glycogen, and phosphocreatine. During anaerobic
exercise such as a sprint, for example, the blood vessels in the
muscle tissue are compressed and the cells are isolated from the
rest of the body (Devlin, supra). This compression restricts the
oxygen supply to the tissue and enforces anaerobic energy
metabolism in the cell. As a result, lactate is generated to
balance the redox potential and must be secreted out of the cell.
In the liver, lactate is converted into glucose. However, rapid
muscle contraction and decreased blood flow to the muscle tissue
cause lactate accumulation during anaerobic exercise and quickly
impairs muscle contraction. During starvation or under high-energy
demands, the glucose and glycogen storage of the muscle tissue
quickly depletes and the energy storage in triacylglycerol
molecules supplied by fatty cells is used to generate ATP.
[0192] To simulate the muscle physiology at steady state,
phosphocreatine kinase reaction, myosin-actin activation mechanism,
and 0-oxidation pathway were included in the central metabolic
network. The physiological function of muscle tissue was simulated
by determining the maximum amount of contraction that is generated
from the energy supplied by glucose, stored glycogen,
phosphocreatine, and supplied fatty acids.
[0193] The metabolic capabilities of the myocyte model were
assessed by first computing the maximum ATP yield on glucose. As
for the central human metabolic network, YATP/glucose was 31.5 mol
ATP/mol glucose. The muscle contraction was also examined with
glucose as the sole carbon source. Maximum muscle contraction with
glucose was computed to be 31.5 mol/mol glucose in aerobic and 2
mol/mol glucose in anaerobic condition. Lactate was secreted as a
byproduct during anaerobic contraction (Yield/actate/glucose=2
mol/mol).
[0194] As lactate accumulates during anaerobic metabolism, its
secretion rate quickly fails to meet the demand to release lactate
into the blood. To simulation the impairment of muscle contraction
in anaerobic exercise, the maximum lactate secretion rate was
constrained to 75%, 50%, 25%, and 0% of its maximum value under
anaerobic condition. The results using these different constraints
are shown in FIG. 12 where the time is shown as an arbitrary unit,
rate of contraction and lactate secretion are in mols per cell mass
per unit time, r corresponds to rate and lac corresponds to
lactate. The results show that as more lactate accumulates in
anaerobic metabolism, the maximum allowable lactate secretion
decreases and maximum muscle contraction decreased
proportionally.
[0195] The muscle contraction was simulated also with stored
glycogen and phosphocreatine as the energy source. The maximum
contraction for glycogen was computed to be 32.5 mol/mol glycogen
in aerobic and 3 mol/mol glycogen in anaerobic condition. The
observed difference between the maximum contraction generated by
glycogen in comparison to glucose arises from the absence of the
phosphorylation or glucokinase step in the first step of
glycolysis. The results of glycogen versus glucose utilization are
illustrated in FIG. 13 where the glycogen utilization pathway is
shown as the thick bent arrow on the left (red) and the glucose
utilization pathway is shown as the thick straight arrow on the
right (blue). The dashed circle (green) represents the endoplasmic
reticulum membrane. The maximum contraction from phosphocreatine
under both aerobic and anaerobic conditions was computed to be 1
mol/mol phosphocreatine. The energy generated from phosphocreatine
is independent of the energy produced through oxidative
phosphorylation and thus was computed to be the same in both
aerobic and anaerobic conditions.
[0196] In addition. .beta.-oxidative pathways in the myocyte tissue
were examined by supplying the network with eicosanoate (n-C20:0),
octadecenoate (C18:1, n-9), and pentadecanoate (C15:0) as examples
of fatty acid oxidation of odd- and even-chain, and saturated and
unsaturated fatty acids. The results are shown in Table 13 and
demonstrate that maximum contraction in the myocyte model was 134
mol/mol for eicosanoate, 118.5 mol/mol for octadecenoate, and 98.5
mol/mol for pentadecanoate. The results also show that on a
carbon-mole basis, all the fatty acids yielded approximately the
same contraction, which was equivalent to ATP yield. Contraction
was observed to be larger in terms of carbon yield than that
generated from glucose (i.e. .about.6.6 mol ATP/C-mol fatty acid in
comparison to 5.3 mol ATP/C-mol glucose). The maximum ATP yield for
palmitate (C16:0) was also computed to be 106 mol ATP/mol
palmitate, which was consistent with the previously calculated
values (Vo et al, supra). One mol of cytosolic protons per mol of
fatty acid was supplied to the network for fatty acid
oxidation.
TABLE-US-00006 TABLE 13 Maximum contraction in the myocyte model
given different fatty acids Maximum Maximum Contraction Contraction
Fatty Acid Abbreviation* (mol/mol fatty acid) (mol/C-mol)
Eicosanoate C20:0 134 6.7 Octadecenoate C18:1, n-9 118.5 6.6
Palmitate C16:0 106 6.6 Pentadecanoate C15:0 98.5 6.6 *Abbreviation
indicates: number of carbons in the fatty acid, number of double
bonds, carbon number where the 1.sup.st double bond appears if the
fatty acid is unsaturated.
[0197] A unit of proton per fatty acid is required in the network
to balance fatty acyl CoA formation in the cell as illustrated in
the following reaction:
TABLE-US-00007 Fatty Acid Fatty Acid + ATP + CoA .fwdarw. Fatty
Acyl-CoA + CoA Ligase: AMP + PPi Adenylate AMP + ATP (2) ADP
Kinase: Inorganic PPi + H.sub.2O .fwdarw. H.sup.+ + (2) Pi
Diphosphatase: Net: Fatty Acid + CoA + (2) ATP + H.sub.2O .fwdarw.
Fatty Acyl-CoA + (2)O ADP + (2) Pi + H.sup.+
[0198] With respect to ATP balance (i.e.
ATP+H.sub.2O.fwdarw.ADP+P.sub.i+H.sup.i), the net reaction has one
mol less H.sub.2O and H'. Water can freely diffuse through the
membrane. However, cell membrane is impermeable to free protons and
thus protons were balanced in all compartments. The proton
requirement in the cell can be fulfilled with a proton-coupled
fatty acid transporter. It has been observed that the proton
electrochemical gradient across the inner membrane plays a crucial
role in energizing the long-chain fatty acid transport apparatus in
E. coli and the proton electrochemical gradient across the inner
membrane is required for optimal fatty acid transport (DiRusso et
al., Mol. Cell. Biochem. 192:41-52 (1999)). Fatty acid transporters
in S. cerevisiae have also been studied, however, no evidence is
currently available on the mechanism of transport. When a proton
coupled fatty acid transporter was used in the model, the
requirement for supplying a mol of proton to the system was
eliminated.
Adipocyte-Myoctye Coupled Functions
[0199] Muscle cells largely rely on their stored glycogen and
phosphocreatine content. During aerobic exercise, however, glucose,
glycogen, and phosphcreatine storage of muscle cells are depleted
and energy generation in myocytes is achieved by fatty acid
oxidation. Lipolysis or lipid degradation proceeds in muscle cells
following the transfer of fatty acids from adipocytes to myocytes
via blood.
[0200] Modeling of multi-cellular metabolism was performed using a
constraint-based approach as described herein where the metabolic
networks of adipocyte and myocyte were combined into a
multi-cellular metabolic model as shown in FIGS. 10-1 through
10-70. The integrated model was assessed by computing the network
energy requirements during anaerobic exercise such as that
corresponding to a sprint and aerobic exercise such as that
corresponding to a marathon. From a purely additive perspective,
combining all of the reactions from the adipocyte model with those
from the myocyte model was initially performed as a sufficient
indicator for the combined network to compute integrated
physiological results. However, with the two models strictly
combined in this manner they were deficient at computing integrated
functions such as those described below and, in particular, the
results described in the "Muscle Contraction in a Marathon" section
below. Addition of buffer systems for bicarbonate and ammonia
allowed the combined model to function more efficiently and
predictably. In retrospect, the inclusion of intra-system reactions
is consistent with the role that, for example, the kidney plays in
integrated metabolic physiology,
[0201] Simulation of an Integrated Model for Muscle Contraction
During a Sprint: The energy requirements of myocytes in a sprint
are extremely high and supplied primarily from the fuel present in
the muscle. In addition, oxygen cannot be transported to the cells
fast enough to trigger an aerobic metabolism. It has been estimated
that only 5% of the energy in a sprint is supplied via oxidative
phosphorylation and the remaining ATP is generated from anaerobic
metabolism from stored glycogen and phosphocreatine (Biochemical
and Physiological Aspects Human Nutrition, Philadelphia, Ed. by M.
H. Stipanuk. W. B. Saunders, (2000)).
[0202] To simulate the metabolic activity of the muscle in a
sprint, the maximum muscle contraction in an aerobic condition was
computed by supplying the multi-cellular model with glucose,
glycogen, and phosphocreatine as shown in Table 14. In addition,
muscle contraction was simulated under anaerobic condition by
constraining the oxygen supply to zero. Maximum contraction was
computed to be the same as in the isolated myocyte model, as
expected, demonstrating that the integrated model retains the
functionalities observed in the single-cell model.
TABLE-US-00008 TABLE 14 Simulation results in the adipocyte-myocyte
integrated model..sup.1 Objective (Cell Aerobic Anaerobic Carbon
Source Type) mol/mol carbon source Glucose Contraction (M) 31.5 2
Glycogen Contraction (M) 32.5 3 Phosphocreatine Contraction (M) 1 1
Glucose ATP synthesis (A) 32.5 -- Glucose TAG synthesis (A) 0.06 --
TAG Glycerol (I) 1* -- TAG supplying C12:0, Contraction (M) 253.9
-- C14:0, C15:0, C16:0, C18:0, C18:1 n-9, and C20:0 *Two protons
were allowed to leave the cytosol (see section "Triacylglycerol
Storage and Utilization in Adipocyte Tissue") -- Not relevant
.sup.1M, myocyte; A, adipocyte; I, intra-system; TAG,
triacylglycerol; C12:0, dodecanoate; C14:0, tetradecanoate; C15:0,
pentadecanoate; C16:0, palmitate, C18:0, octadecanoate; C18:1 n-9,
octadecenoate; C20:0, eicosanoate
[0203] Simulation of an Integrated Model for Muscle Contraction
During a Marathon: The total energy expenditure in a marathon is
about 12,000 kJ or 2868 kcal, which is equivalent to burning about
750 g of carbohydrate or 330 g of fat (Stipanuk, supra). Since the
total stored carbohydrate in the body is only about 400 to 900 g.
the mobilized fatty acids from adipose tissue provide an important
part of the supplied energy to the muscle cells in an aerobic
metabolism and especially in a marathon.
[0204] To simulate the aerobic oxidation of fatty acid in the
muscle cells, the integrated model was first demonstrated to be
able to synthesize and store triacylglycerol in the adipocyte
compartment when supplied by glucose. As for the single cell model,
the integrated adipocyte-myocyte network was able to store TAG in
adipocyte compartment. The results are shown in Table 14. In
addition, TAG degradation and fatty acid mobilization to the blood
was simulated by maximizing glycerol secretion in the intra-system
space generated from the stored TAG in adipocyte. As with the
single cell model, TAG hydrolysis was simulated with the integrated
adipocyte-myocyte model and maximum glycerol secretion rate was
shown to be the same.
[0205] To demonstrate the coupled function of the two cell types,
muscle contraction in an aerobic exercise was simulated by
constraining all other alternative carbon sources including
glucose, stored glycogen, and phosphocreatine to zero and supplying
adipocyte with stored triacylglycerol as an energy source. Exchange
fluxes were included to ensure the proper transfer of fatty acids
between the two models, The maximum muscle contraction in the
network that contains .beta.-oxidative pathways for fatty acids
C12:0, C14:0, C15:0, C16:0, C18:0, C18:1 n-9, and C20:0 was
simulated and computed to be 253.9 mol/mol TAG. The total
contraction in this simulation is the sum of maximum contraction
that is generated if the model was supplied with each fatty acid
individually. The results from using the integrated model
demonstrated that energy generated in the muscle cell from
triacylglycerol is produced in an additive fashion and metabolite
balance in the two cell types does not reduce the energy production
in the cell.
[0206] These studies further demonstrate the application of a
constraint-based approach to modeling multi-cellular integrated
metabolic models. The results also indicate that modeling
multi-cellular networks can be optimized by incorporating
intra-system reactions such as the bicarbonate and ammonia buffer
systems into the integrated adipocyte-myocyte model. The
reconstructed models and simulation results also demonstrated that
metabolic functions of various cell types can be studied,
understood and reproduced using the methods of the invention.
Furthermore, coupling of the functions of multiple cell types in a
system was demonstrated through the transport of various
metabolites and the coupled function of different cell types were
studied by imposing biologically appropriate objective function.
Finally, the ability to predict further network modifications, such
as the transport mechanism of fatty acids into myocyte, using the
reconstructed models also was demonstrated. These results also
indicate that multi-cellular modeling can be extended to the
modeling of more than two cells and which correspond to various
cell types including the same specie or among multiple different
species, tissues, organs, and whole body by including additional
genomic, biochemical, physiological, and high throughput
datasets.
[0207] Throughout this application various publications have been
referenced within parentheses. The disclosures of these
publications in their entireties are hereby incorporated by
reference in this application in order to more fully describe the
state of the art to which this invention pertains.
[0208] Although the invention has been described with reference to
the disclosed embodiments, those skilled in the art will readily
appreciate that the specific examples and studies detailed above
are only illustrative of the invention. It should be understood
that various modifications can be made without departing from the
spirit of the invention. Accordingly, the invention is limited only
by the following claims.
TABLE-US-00009 TABLE 1 Locus ID Gene Ab. Reaction Stoichiometry
E.C. 1. Carbohydrate Metabolism 1.1 Glycolysis/Gluconeogenesis
[PATH:hsa00010] 3098 HK1 GLC + ATP -> G6P + ADP 2.7.1.1 3099 HK2
GLC + ATP -> G6P + ADP 2.7.1.1 3101 HK3 GLC + ATP -> G6P +
ADP 2.7.1.1 2645 GCK, HK4, MODY2, NIDDM GLC + ATP -> G6P + ADP
2.7.1.2 2538 G6PC, G6PT G6P + H2O -> GLC + PI 3.1.3.9 2821 GPI
G6P <-> F6P 5.3.1.9 5211 PFKL F6P + ATP -> FDP + ADP
2.7.1.11 5213 PFKM F6P + ATP -> FDP + ADP 2.7.1.11 5214 PFKP,
PFK-C F6P + ATP -> FDP + ADP 2.7.1.11 5215 PFKX F6P + ATP ->
FDP + ADP 2.7.1.11 2203 FBP1, FBP FDP + H2O -> F6P + PI 3.1.3.11
8789 FBP2 FDP + H2O -> F6P + PI 3.1.3.11 226 ALDOA FDP <->
T3P2 + T3P1 4.1.2.13 229 ALDOB FDP <-> T3P2 + T3P1 4.1.2.13
230 ALDOC FDP <-> T3P2 + T3P1 4.1.2.13 7167 TPI1 T3P2
<-> T3P1 5.3.1.1 2597 GAPD, GAPDH T3P1 + PI + NAD <->
NADH + 13PDG 1.2.1.12 26330 GAPDS, GAPDH-2 T3P1 + PI + NAD
<-> NADH + 13PDG 1.2.1.12 5230 PGK1, PGKA 13PDG + ADP
<-> 3PG + ATP 2.7.23 5233 PGK2 13PDG + ADP <-> 3PG +
ATP 2.7.2.3 5223 PGAM1, PGAMA 13PDG -> 23PDG 5.4.2.4 23PDG + H2O
-> 3PG + PI 3.1.3.13 3PG <-> 2PG 5.4.2.1 5224 PGAM2, PGAMM
13PDG <-> 23PDG 5.4.2.4 23PDG + H2O -> 3PG + PI 3.1.3.13
3PG <-> 2PG 5.4.2.1 669 BPGM 13PDG <-> 23PDG 5.4.2.4
23PDG + H2O <-> 3PG + PI 3.1.3.13 3PG <-> 2PG 5.4.2.1
2023 EN01, PPH, ENO1L1 2PG <-> PEP + H2O 4.2.1.11 2026 EN02
2PG <-> PEP + H2O 4.2.1.11 2027 EN03 2PG <-> PEP + H2O
4.2.1.11 26237 ENOIB 2PG <-> PEP + H2O 4.2.1.11 5313 PKLR,
PK1 PEP + ADP -> PYR + ATP 2.7.1.40 5315 PKM2, PK3, THBP1, OIP3
PEP + ADP -> PYR + ATP 2.7.1.40 5160 PDHA1, PHE1A, PDHA PYRm +
COAm + NADm -> +NADHm + CO2m + ACCOAm 1.2.4.1 5161 PDHA2, PDHAL
PYRm + COAm + NADm -> +NADHm + CO2m + ACCOAm 12.4.1 5162 PDHB
PYRm + COAm + NADm -> +NADHm + CO2m + ACCOAm 1.2.4.1 1737 DLAT,
DLTA, PDC-E2 PYRm + COAm + NADm -> +NADHm + CO2m + ACCOAm
2.3.1.12 8050 PDX1, E3BP PYRm + COAm + NADm -> +NADHm + CO2m +
ACCOAm 2.3.1.12 3939 LDHA, LDH1 NAD + LAC <-> PYR + NADH
1.1.1.27 3945 LDHB NAD + LAC <-> PYR + NADH 1.1.1.27 3948
LDHC, LDH3 NAD + LAC <-> PYR + NADH 1.1.1.27 5236 PGM1 G1P
<-> G6P 5.4.2.2 5237 PGM2 G1P <-> G6P 5.4.2.2 5238 PGM3
G1P <-> G6P 5.4.2.2 1738 DLD, LAD, PHE3, DLDH, E3 DLIPOm +
FADm <-> LIPOm + FADH2m 1.8.1.4 124 ADH1 ETH + NAD <->
ACAL + NADH 1.1.1.1 125 ADH2 ETH + NAD <-> ACAL + NADH
1.1.1.1 126 ADH3 ETH + NAD <-> ACAL + NADH 1.1.1.1 127 ADH4
ETH + NAD <-> ACAL + NADH 1.1.1.1 128 ADH5 FALD + RGT + NAD
<-> FGT + NADH 1.2.1.1 ETH + NAD <-> ACAL + NADH
1.1.1.1 130 ADH6 ETH + WAD <-> ACAL + NADH 1.1.1.1 131 ADH7
ETH + NAD <-> ACAL + NADH 1.1.1.1 10327 AKR1A1, ALR, ALDR1
1.1.12 97 ACYP1 3.6.1.7 98 ACYP2 3.6.1.7 1.2 Citrate cycle (TCA
cycle) PATH:hsa00020 1431 CS ACCOAm + OAm + H2Om -> COAm + CITm
4.1.3.7 48 ACO1, IREB1, IRP1 CIT <-> ICIT 4.2.1.3 50 ACO2
CITm <-> ICITm 4.2.1.3 3417 IDH1 ICIT + NADP -> NADPH +
CO2 + AKG 1.1.1.42 3418 IDH2 ICITm + NADPm -> NADPHm + CO2m +
AKGm 1.1.1.42 3419 IDH3A ICITm + NADm -> CO2m + NADHm + AKGm
1,1.1.41 3420 IDH3B ICITm + NADm -> CO2m + NADHm + AKGm 1.1.1.41
3421 IDH3G ICITm + NADm -> CO2m + NADHm + AKGm 1.1.1.41 4967
OGOH AKGm + NADm + COAm -> CO2m + NADHm + SUCCOAm 1.2.4.2 1743
DLST, DLTS AKGm + NADm + COAm -> CO2m + NADHm + SUCCOAm 2.3.1.61
8802 SUCLG1, SUCLA1 GTPm + SUCCm + COAm <-> GDPm + Plm +
SUCCOAm 6.2.1.4 8603 SUCLA2 ATPm + SUCCm + COAm <-> ADPm +
Plm + SUCCOAm 6.2.1.4 2271 FH FUMm + H2Om <-> MALm 4.2.1.2
4190 MDH1 MAl + NAD <-> NADH + OA 1.1.1.37 4191 MDH2 MALm +
NADm <-> NADHm + OAm 1.1.1.37 5091 PC, PCB PYRm + ATPm + CO2m
-> ADPm + OAm + Plm 6.4.1.1 47 ACLY, ATPCL, CLATP ATP + CIT +
COA + H2O -> ADP + PI + ACCOA + OA 4.1.3.8 3657 5105 PCK1 OA +
GTP -> PEP + GDP + CO2 4.1.1.32 5106 PCK2, PEPCK OAm + GTPm
-> PEPm + GDPm + CO2m 4.1.1.32 1.3 Pentose phosphate cycle
PATH:hsa00030 2539 G6PD, G6PD1 G6P + NADP <-> D6PGL + NADPH
1.1.1.49 9563 H6PD 1.1.1.47 D6PGL + H2O -> D6PGC 3.1.1.31 25796
PGLS, 6PGL D6PGL + H2O -> D6PGC 3.1.1.31 5226 PGD D6PGC + NADP
-> NADPH + CO2 + RL5P 1.1.1.44 6120 RPE RL5P <-> X5P
5.1.3.1 7086 TKT R5P + X5P <-> T3P1 + S7P 2.2.1.1 X5P + E4P
<-> F6P + T3P1 8277 TKTL1, TKR, TKT2 R5P + X5P <-> T3P1
+ S7P 2.2.1.1 X5P + E4P <-> F6P + T3P1 6868 TALDO1 T3P1 + S7P
<-> E4P + F6P 2.2.1.2 5631 PRPS1, PRS I, PRS, I R5P + ATP
<-> PRPP + AMP 2.7.6.1 5634 PRPS2, PRS II, PRS, II R5P + ATP
<-> PRPP + AMP 2.7.6.1 2663 GDH 1.1.1.47 1.4 Pentose and
glucuronate interconversions PATH:hsa00040 231 AKR1B1 , AR, ALDR1,
ADR 1.1.1.21 7359 UGP1 G1P + UTP -> UDPG + PPI 2.1.7.9 7360
UGP2, UGPP2 G1P + UTP-> UDPG + PPI 2.7.7.9 7358 UGDH, UDPGDH
1.1.1.22 10720 UGT2B11 2.4.1.17 54658 UGT1A1, UGT1A, GNT1, UGT1
2.4.1.17 7361 UGT1A, UGT1, UGT1A 2.4.1.17 7362 UGT2B, UGT2, UGT2B
2.4.1.17 7363 UGT2B4, UGT2B11 2.4.1.17 7364 UGT2B7, UGT2B9 2.4.1.17
7365 UGT2B10 2.4.1.17 7366 UGT2B15, UGT2B8 2.4.1.17 7367 UGT2B17
2.4.1.17 13 AADAC, DAC 3.1.1.-- 3991 LIPE, LHS, HSL 3.1.1.-- 1.5
Fructose and mannose metabolism PATH:hsa00051 4351 MPI, PMI1 MAN6P
<-> F6P 5.3.1.8 5372 PMM1 MAN6P <-> MAN1P 5.4.28 5373
PMM2, CDG1, CDGS MAN6P <-> MAN1P 5.4.2.8 2762 GMDS 4.2.1.47
8790 FPGT, GFPP 2.7.7.30 5207 PFKFB1, PFRX ATP + F6P -> ADP +
F26P 2.7.1.105 F26P -> F6P + PI 3.1.3.46 5208 PFKFB2 ATP + F6P
-> ADP + F26P 2.7.1.105 F26P -> F6P + PI 3.1.3.46 5209 PFKF83
ATP + F6P -> ADP + F26P 2.7.1.105 F26P -> F6P + PI 3.1.3.46
5210 PFKFB4 ATP + F6P -> ADP + F26P 2.7.1.105 F26P -> F6P +
PI 3.1.3.46 3795 KHK 2.7.1.3 6652 SORD DSOT + NAD -> FRU + NADH
1.1.1.14 2526 FUT4, FCT3A, FUC-TIV 2.4.1.-- 2529 FUT7 2.4.1.-- 3036
HAST HAS 2.4.1.-- 3037 HAS2 2.4.1.-- 8473 OGT, O-GLCNAC 2.4.1.--
51144 LOC51144 1.1.1.-- 1.6 Galactose metabolism PATH:hsa00052 2584
GALK1, GALK, GLAC + ATP -> GAL1P + ADP 2.7.1.6 2585 GALK2, GK2
GLAC + ATP -> GAL1P + ADP 2.7.1.6 2592 GALT UTP + GAL1P
<-> PPI + UDPGAL 2.7.7.10 2582 GALE UDPGAL <-> UDPG
5.1.3.2 2720 GLB1 3.2.1.23 3938 LCT, LAC 3.2.1.62 3.2.1.108 2683
B4GALT1, GGT82, BETA4GAL-T1, 2.4.1.90 GT1, GTB 2.4.1.38 2.4.1.22
3906 LALBA 2.4.1.22 2717 GLA, GALA MELI -> GLC + GLAC 3.2.1.22
2548 GAA MLT -> 2 GLC 3.2.1.20 6DGLC -> GLAC + GLC 2594 GANAB
MLT -> 2 GLC 3.2.1.20 6DGLC -> GLAC + GLC 2595 GANC MLT ->
2 GLC 3.2.1.20 6DGLC -> GLAC + GLC 8972 MGAM, MG, MGA MLT ->
2 GLC 3.2.1.20 6DGLC -> GLAC + GLC 3.2.1.3 1.7 Ascorbate and
aldarate metabolism PATH:hsa00053. 216 ALDH1, PUMB1 ACAL + NAD
-> NADH + AC 1.2.1.3 217 ALDH2 ACALm + NADm -> NADHm + ACm
1.2.1.3 219 ALDH5, ALDHX 1.2.1.3 223 ALDH9, E3 1.2.1.3 1.2.1.19 224
ALDH10, FALDH, SLS 1.2.1.3 8854 RALDH2 1.2.1.3 1591 CYP24
1.14.--.-- 1592 CYP26A1, P450RAI 1.14.--.-- 1593 CYP27A1, CTX,
CYP27 1.14.--.-- 1594 CYP27B1, POOR, VDD1, VDR, CYP1, 1.14.--.--
VDDR, I, P450C1 1.8 Pyruvate metabolism PATH:hsa00620 54988
FLJ20581 ATP + AC + COA -> AMP + PPI + ACCOA 6.2.1.1 31 ACACA,
ACAC, ACC ACCOA + ATP + CO2 <-> MALCOA + ADP + PI + H 6.4.1.2
6.3.4.14 32 ACACB, ACCB, HACC275, ACC2 ACCOA + ATP + CO2 <->
MALCOA + ADP + PI + H 6.4.1.2 6.6.4.14 2739 GLO1, GLYI RGT + MTHGXL
<-> LGT 4.4.1.5 3029 HAGH, GLO2 LGT -> RGT + LAC 3.1.2.6
2223 FDH FALD + RGT + NAD <-> FGT + NADH 1.2.1.1 9380 GRHPR,
GLXR 1.1.1.79 4200 ME2 MALm + NADm -> CO2m + NADHm + PYRm
1.1.1.38 10873 ME3 MALm + NADPm -> CO2m + NADPHm + PYRm 1.1.1.40
29897 HUMNDME MAL + NADP -> CO2 + NADPH + PYR 1.1.1.40 4199 ME1
MAL + NADP -> CO2 + NADPH + PYR 1.1.1.40 38 ACAT1, ACAT, T2,
THIL, MAT 2 ACCOAm <-> COAm + AACCOAm 2.3.1.9 39 ACAT2 2
ACCOAm <-> COAm + AACCOAm 2.3.1.9 1.9 Glyoxylate and
dicarboxylate metabolism PATH:hsa00630 5240 PGP 3.1.3.18 2758 GLYD
3HPm + NADHm -> NADm + GLYAm 1.1.1.29 10797 MTHFD2, NMDMC METHF
<-> FTHF 3.5.4.9 METTHF + NAD -> METHF + NADH 1.5.1.15
4522 MTHFD1 METTHF + NADP <-> METHF + NADPH 1.5.1.15 METHF
<-> FTHF 3.5.4.9 THF + FOR + ATP -> ADP + PI + FTHF
6.3.4.3 1.10 Propanoate metabolism PATH:hsa00640 34 ACADM, MCAD
MBCOAm + FADm -> MCCOAm + FADH2m 1.3.99.3 IBCOAm + FADm ->
MACOAm + FADH2m IVCOAm + FADm -> MCRCOAm + FADH2m 36 ACADSB
MBCOAm + FADm -> MCCOAm + FADH2m 1.3.99.3 IBCOAm + FADm ->
MACOAm + FADH2m IVCOAm + FADm -> MCRCOAm + FADH2m 1892 ECHS1,
SCEH MACOAm + H2Om -> HIBCOAm 4.2.1.17 MCCOAm + H2Om ->
MHVCOAm 1962 EHHADH MHVCOAm + NADm -> MAACOAm + NADHm 1.1.1.35
HIBm + NADm -> MMAm + NADHm MACOAm + H2Om -> HIBCOAm 4.2.1.17
MCCOAm + H2Om -> MHVCOAm 3030 HADHA, MTPA, GBP MHVCOAm + NADm
-> MAACOAm + NADHm 1.1.1.35 HIBm + NADm -> MMAm + NADHm
MACOAm + H2Om -> HIBCOAm 4.2.1.17 MCCOAm + H2Om -> MHVCOAm
C160CARm + COAm + FADm + NADm -> FADH2m + NADHm + 1.1.1.35
C140COAm + ACCOAm 4.2.1.17 23417 MLYCD, MCD 4.1.1.9 18 ABAT, GABAT
GABA + AKG -> SUCCSAL + GLU 2.6.1.19 5095 PCCA PROPCOAm + CO2m +
ATPm -> ADPm + PIm + DMMCOAm 6.4.1.3 5096 PCCB PROPCOAm + CO2m +
ATPm -> ADPm + PIm + DMMCOAm 6.4.1.3 4594 MUT, MCM LMMCOAm ->
SUCCOAm 5.4.99.2 4329 MMSDH MMAm + COAm + NADm -> NADHm + CO2m +
PROPCOAm 1.2.1.27 8523 FACVL1, VLCS, VLACS 6.2.1.-- 1.11 Butanoate
metabolism PATH:hsa00650 3028 HADH2, ERAB C140COAm + 7 COAm + 7
FADm + 7 NADm -> 7 FADH2m + 7 1.1.1.35 NADHm + 7 ACCOAm 3033
HADHSC, SCHAD 1.1.1.35 35 ACADS, SCAD MBCOAm + FADm -> MCCOAm +
FADH2m 1.3.99.2 IBCOAm + FADm -> MACOAm + FADH2m 7915 ALDH5A1,
SSADH, SSDH 1.2.1.24 2571 GAD1, GAD, GAD67, GAD25 GLU -> GABA +
CO2 4.1.1.15 2572 GAD2 GLU -> GABA + CO2 4.1.1.15 2573 GAD3 GLU
-> GABA + CO2 4.1.1.15 3157 HMGCS1, HMGCS H3MCOA + COA <->
ACCOA + AACCOA 4.1.3.5 3158 HMGCS2 H3MCOA + COA <-> ACCOA +
AACCOA 4.1.3.5 3155 HMGCL, HL H3MCOAm -> ACCOAm + ACTACm 4.1.3.4
5019 OXCT 2.8.3.5 622 BDH 3HBm + NADm -> NADHm + Hm + ACTACm
1.1.1.30 1629 DBT, BCATE2 OMVALm + COAm + NADm -> MBCOAm + NADHm
+ CO2m 2.3.1.-- OIVALm + COAm + NADm -> IBCOAm + NADHm + CO2m
OICAPm + COAm + NADHm -> IVCOAm + NADHm + CO2m
1.13 Inositol metabolism PATH:hsa00031 2. Energy Metabolism 2.1
Oxidative phosphorylation PATH:hsa00190 4535 MTND1 NADHm + Qm + 4
Hm -> QH2m + NADm + 4 H 1.6.5.3 4636 MTND2 NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.5.3 4537 MTND3 NADHm + Qm + 4 Hm ->
QH2m + NADm + 4 H 1.6.5.3 4538 MTND4 NADHm + Qm + 4 Hm -> QH2m +
NADm + 4 H 1.6.5.3 4539 MTND4L NADHm + Qm + 4 Hm -> QH2m + NADm
+ 4 H 1.6.5.3 4240 MIND5 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.5.3 4541 MTND8 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.5.3 4694 NDUFA1, MWFE NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.5.3 4695 NDUFA2, B8 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4696
NDUFA3, B9 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm
+ Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4697 NDUFA4, MLRQ
NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.99.3 4698 NDUFA5, UQOR13, 813 NADHm +
Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm ->
QH2m + NADm + 4 H 1.6.99.3 4700 NOUFA6, B14 NADHm + Qm + 4 Hm ->
QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.99.3 4701 NOUFA7, B14.5a, B14.5A NADHm + Qm + 4 Hm -> QH2m +
NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.99.3 4702 NDUFA8, PGIV NADHm + Qm + 4 Hm -> QH2m + NADm + 4
H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1,6.99.3 4704
NDUFA9, NDUFS2L NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3
NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4705 NDUFA10
NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.99.3 4706 NDUFAB1, SDAP NADHm + Qm + 4
Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m +
NADm + 4 H 1.6.99.3 4707 NDUFB1, MNLL, CI-SGDH NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm
+ 4 H 1.6.99.3 4708 NDUFB2, AGGG NADHm + Qm + 4 Hm -> QH2m +
NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.99.3 4709 NDUFB3, B12 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4710
NOUFB4, B15 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm
+ Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4711 NDUFB5, SGDH
NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.99.3 4712 NDUF86, B17 NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm
+ 4 H 1.6.99.3 4713 NDUFB7, B18 NADHm + Qm + 4 Hm -> QH2m + NADm
+ 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3
4714 NDUFB8, ASHI NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3
NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4715 NDUFB9,
UQOR22, B22 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm
+ Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4716 NDUFB10, PDSW
NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.99.3 4717 NDUFC1, KFYI NADHm + Qm + 4
Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m +
NADm + 4 H 1.6.99.3 4718 NDUFC2, B14.5b, B14.58 NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm
+ 4 H 1.6.99.3 4724 NDUFS4, AQDQ NADHm + Qm + 4 Hm -> QH2m +
NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.99.3 4725 NDUFS5 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H
1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4726
NDUFS6 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm
+ 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4731 NDUFV3 NADHm + Qm + 4
Hm -> QH2m + NADm + 4 H 1.6.5.3 4127 NDUFS7, PSST NADHm + Qm + 4
Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m +
NADm + 4 H 1.6.99.3 4722 NDUFS3 NADHm + Qm + 4 Hm -> QH2m + NADm
+ 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3
4720 NDUFS2 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 4729
NDUFV2 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm
+ 4 Hm -> QH2m + NADm + 4 H 1.6.99.3 4723 NDUFV1, UQOR1 NADHm +
Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.5.3 NADHm + Qm + 4 Hm ->
QH2m + NADm + 4 H 1.6.99.3 4719 NDUFS1, PRO1304 NADHm + Qm + 4 Hm
-> QH2m + NADm + 4 H 1.6.99.3 NADHm + Qm + 4 Hm -> QH2m +
NADm + 4 H 1.6.5.3 4728 NDUFS8 NADHm + Qm + 4 Hm -> QH2m + NADm
+ 4 H 1.6.5.3 NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 1.6.99.3
6391 SDHC SUCCm + FADm <-> FUMm + FADH2m 1.3.5.1 FADH2m + Qm
<-> FADm + QH2m 6392 SDHD, CBT1, PGL, PGL1 SUCCm + FADm
<-> FUMm + FADH2m 1.3.5.1 FADH2m + Qm <-> FADm + QH2m
6389 SDHA, SDH2, SDHF, FP SUCCm + FADm <-> FUMm + FADH2m
1.3.5.1 FADH2m + Qm <-> FADm + QH2m 6390 SDHB, SDH1, IP, SDH
SUCCm + FADm <-> FUMm + FADH2m 1.3.5.1 FADH2m + Qm <->
FADm + QH2m 7386 UQCRFS1, RIS1 O2m + 4 FEROm + 4 Hm -> 4 FERIm +
2 H2Om + 4 H 1.10.2.2 4519 MTCYB O2m + 4 FEROm + 4 Hm -> 4 FERIm
+ 2 H2Om + 4 H 1.10.2.2 1537 CYC1 O2m + 4 FEROm + 4 Hm -> 4
FERIm + 2 H2Om + 4 H 1.10.2.2 7384 UQCRC1- D3S3191 O2m + 4 FEROm +
4 Hm -> 4 FERIm + 2 H2Om + 4 H 1.10.2.2 7385 UQCRC2 O2m + 4
FEROm + 4 Hm -> 4 FERIm + 2 H2Om + 4 H 1.10.2.2 7388 UQCRH O2m +
4 FEROm + 4 Hm -> 4 FERIm + 2 H2Om + 4 H 1.10.2.2 7381 UQCRB,
QPC, UQBP, QP-C O2m + 4 FEROm + 4 Hm -> 4 FERIm + 2 H2Om + 4 H
1.10.2.2 27089 QP-C O2m + 4 FEROm + 4 Hm -> 4 FERIm + 2 H2Om + 4
H 1.10.2.2 10975 UQCR O2m + 4 FEROm + 4 Hm -> 4 FERIm + 2 H2Om +
4 H 1.10.2.2 1333 COX5BL4 QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm
+ 4 H 1.9.3.1 4514 MTCO3 QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm +
4 H 1.9.3.1 4512 MTCO1 QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4
H 1.9.3.1 4513 MTCO2 QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4 H
1.9.3.1 1329 COX5B QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4 H
1.9.3.1 1327 COX4 QH2m + 2 FERIm + 4 Hm -> Om + 2 FEROm + 4 H
1.9.3.1 1337 COX6A1, COX6A QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm
+ 4 H 1.9.3.1 1339 COX6A2 QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm
+ 4 H 1.9.3.1 1340 COX6B QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm +
4 H 1.9.3.1 1345 COX6C QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4
H 1.9.3.1 9377 COX5A, COX, VA, COX-VA QH2m + 2 FERIm + 4 Hm ->
Qm + 2 FEROm + 4 H 1.9.3.1 1346 COX7A1, COX7AM, COX7A QH2m + 2
FERIm + 4 Hm -> Qm + 2 FEROm + 4 H 1.9.3.1 1347 COX7A2, COX
VIIa-L QH2m + 2 FERIm + 4 Hm -> Om + 2 FEROm + 4 H 1.9.3.1 1346
COX7A3 QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4 H 1.9.3.1 1349
COX7B QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4 H 1.9.3.1 9167
COX7A2L, COX7RP, EB1 QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4 H
1.9.3.1 1350 COX7C QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4 H
1.9.3.1 1351 COX8, COX VIII QH2m + 2 FERIm + 4 Hm -> Qm + 2
FEROm + 4 H 1.9.3.1 4508 MTATP6 ADPm + Pim + 3 H -> ATPm + 3 Hm
+ H2Om 3.6.1.34 4509 MTATP8 ADPm + Pim + 3 H -> ATPm + 3 Hm +
H2Om 3.6.1.34 499 ATPSA2 ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om
3.6.1.34 507 ATP5BL1, ATPSBL1 ADPm + Pim + 3 H -> ATPm + 3 Hm +
H2Om 3.6.1.34 508 ATP5BL2, ATPSBL2 ADPm + Pim + 3 H -> ATPm + 3
Hm + H2Om 3.6.1.34 519 ATP5H ADPm + Pim + 3 H -> ATPm + 3 Hm +
H2Om 3.6.1.34 537 ATP6S1, ORF, VATPS1, XAP-3 ADPm + Pim + 3 H ->
ATPm + 3 Hm + H2Om 3.6.1.34 514 ATP5E ADPm + Pim + 3 H -> ATPm +
3 Hm + H2Om 3.6.1.34 513 ATP5D ADPm + Pim + 3 H -> ATPm + 3 Hm +
H2Om 3.6.1.34 506 ATP5B, ATPSB ADPm + Pim + 3 H -> ATPm + 3 Hm +
H2Om 3.6.1.34 509 ATP5C1, ATP5C ADPm + Pim + 3 H -> ATPm + 3 Hm
+ H2Om 3.6.1.34 498 ATP5A1, ATP5A, ATPM, OMR, HATP1 ADPm + Pim + 3
H -> ATPm + 3 Hm + H2Om 3.6.1.34 539 ATP50, ATPO, OSCP ADPm +
Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 516 ATP5G1, ATP5G ADPm
+ Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 517 ATP5G2 ADPm + Pim
+ 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 518 ATP5G3 ADPm + Pim + 3 H
-> ATPm + 3 Hm + H2Om 3.6.1.34 515 ATP5F1 ADPm + Pim + 3 H ->
ATPm + 3 Hm + H2Om 3.6.1.34 521 ATP5I ADPm + Pim + 3 H -> ATPm +
3 Hm + H2Om 3.6.1.34 522 ATP5J, ATP5A, ATPM, ATP5 ADPm + Pim + 3 H
-> ATPm + 3 Hm + H2Om 3.6.1.34 9551 ATP5J2, ATP5JL, F1FO-ATPASE
ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 10476 ATP5JD
ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 10632 ATP5JG
ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 9296 ATP6S14
ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 528 ATP6D ADPm +
Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 523 ATP6A1, VPP2 ADPm +
Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 524 ATP6A2, VPP2 ADPm +
Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 525 ATP6B1, VPP3, VATB
ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 526 ATP6B2, VPP3
ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 523 ATP6E ADPm +
Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 527 ATP6C, ATPL ADPm +
Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 533 ATP6F ADPm + Pim +
3 H -> ATPm + 3 Hm + H2Om 3.6.1.34 10312 TdRG1, TIRC7, OC-116,
CC-116 kDa, ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om 3.6.1.34
OC-116 KDA, ATP6N1C 23545 TJ6 ADPm + Pim + 3 H -> ATPm + 3 Hm +
H2Om 3.6.1.34 50617 ATP6N1B ADPm + Pim + 3 H -> ATPm + 3 Hm +
H2Om 3.6.1.34 535 ATP6N1 ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om
3.6.1.34 51382 VATD ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om
3.6.1.34 8992 ATP6H ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om
3.6.1.34 9550 ATP6J ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om
3.6.1.34 51606 LOC51606 ADPm + Pim + 3 H -> ATPm + 3 Hm + H2Om
3.6.1.34 495 ATP4A, ATP6A ATP + H + Kxt + H2O <-> ADP + PI +
Hext + K 3.6.1.36 496 ATP4B, ATP6B ATP + H + Kxt + H2O <->
ADP + PI + Hext + K 3.6.1.36 476 ATP1A1 ATP + 3 NA + 2 Kxt + H2O
<-> ADP + 3 NAxt + 2 K + PI 3.6.1.37 477 ATP1A2 ATP + 3 NA +
2 Kxt + H2O <-> ADP + 3 NAxt + 2 K + PI 3.6.1.37 478 ATP1A3
ATP + 3 NA + 2 Kxt + H2O <-> ADP + 3 NAxt + 2 K + PI 3.6.1.37
479 ATP1AL1 ATP + 3 NA + 2 Kxt + H2O <-> ADP + 3 NAxt + 2 K +
PI 3.6.1.37 23439 ATP1B4 ATP + 3 NA + 2 Kxt + H2O <-> ADP + 3
NAxt + 2 K + PI 3.6.1.37 481 ATP1B1, ATP1B ATP + 3 NA + 2 Kxt + H2O
<-> ADP + 3 NAxt + 2 K + PI 3.6.1.37 482 ATP1B2, AMOG ATP + 3
NA + 2 Kxt + H2O <-> ADP + 3 NAxt + 2 K + PI 3.6.1.37 483
ATP1B3 ATP + 3 NA + 2 Kxt + H2O <-> ADP + 3 NAxt + 2 K + PI
3.6.1.37 27032 ATP2C1, ATP2C1A, PMR1 ATP + 2 CA + H20 <-> ADP
+ PI + 2 CAxt 3.6.1.38 487 ATP2A1, SERCA1, ATP2A ATP + 2 CA + H20
<-> ADP + PI + 2 CAxt 3.6.1.38 488 ATP2A2, ATP2B, SERCA2,
DAR, DD ATP + 2 CA + H20 <-> ADP + PI + 2 CAxt 3.6.1.38 489
ATP2A3, SERCA3 ATP + 2 CA + H20 <-> ADP + PI + 2 CAxt
3.6.1.38 490 ATP2B1, PMCA1 ATP + 2 CA + H20 <-> ADP + PI + 2
CAxt 3.6.1.38 491 ATP2B2, PMCA2 ATP + 2 CA + H20 <-> ADP + PI
+ 2 CAxt 3.6.1.38 492 ATP2B3, PMCA3 ATP + 2 CA + H20 <-> ADP
+ PI + 2 CAxt 3.6.1.38 493 ATP2B4, ATP2B2, PMCA4 ATP + 2 CA + H20
<-> ADP + PI + 2 CAxt 3.6.1.38 538 ATP7A, MK, MNK, OHS ATP +
H2O + Cu2 -> ADP + PI + Cu2xt 3.6.3.4 540 ATP7B, WND ATP + H2O +
Cu2 -> ADP + PI + Cu2xt 3.6.3.4 5464 PP, SID6-8061 PPI -> 2
PI 3.6.1.1 2.2 Photosynthesis PATH:hsa00195 2.3 Carbon fixation
PATH:hsa00710 2805 GOT1 OAm + GLUm <-> ASPm + AKGm 2.6.1.1
2806 GOT2 OA + GLU <-> ASP + AKG 2.6.1.1 2875 GPT PYR + GLU
<-> AKG + ALA 2.6.1.2 2.4 Reductive carboxyiate cycle (CO2
fixation) PATH:hsa00720 2.5 Methane metabolism PATH:hsa00680 847
CAT 2 H202 -> O2 1.11.1.6 4025 LPO, SPO 1.11.1.7 4353 MPO
1.11.1.7 8288 EPX, EPX-PEN, EPO, EPP 1.11.1.7 9588 KIAA0106, AOP2
1.11.1.7 6470 SHMT1, CSHMT THF + SER <-> GLY + METTHF 2.1.1.1
6472 SHMT2, GLYA, SHMT THFm + SERm <-> GlYm + METTHFm 2.1.2.1
51004 LOC51004 2OPMPm + O2m -> 2OPMBm 1.14.13.-- 2OPMMBm + O2m
-> 2OMHMBm 9420 CYP7B1 2OPMPm + O2m -> 2OPMBm 1.14.13.--
2OPMMBm + O2m -> 2OMHM8m 2.6 Nitrogen metabolism PATH:hsa00910
11238 CA5B 4.2.1.1 23632 CA14 4.2.1.1 759 CA1 4.2.1.1 760 CA2
4.2.1.1 761 CA3, CAIII 4.2.1.1 762 CA4, CAIV 4.2.1.1 763 CA5A, CA5,
CAV, CAVA 4.2.1.1 765 CA6 4.2.1.1 766 CA7 4.2.1.1 767 CA8, CALS,
CARP 4.2.1.1 768 CA9, MN 4.2.1.1 770 CA11, CARP2 4.2.1.1 771 CA12
4.2.1.1 1373 CPS1 GLUm + CO2m + 2 ATPm -> 2 ADPm + 2 Plm + CAPm
6.3.4.16 275 AMT GLYm + THFm + NADm <-> METTHFm + NADHm +
CO2m + 2.1.2.10 NH3m 3034 HAL, HSTD, HIS HIS -> NH3 + URO
4.3.1.3 2746 GLUD1, GLUD AKGm + NADHm + NH3m <-> NADm + H2Om
+ GLUm 1.4.1.3 AKGm + NADPHm + NH3m <-> NADPm + H2Om + GLUm
8307 GLUD2 AKGm + NADHm + NH3m <-> NADm + H2Om + GLUm 1.4.1.3
AKGm + NADPHm + NH3m <-> NADPm + H2Om + GLUm 2752 GLUL, GLNS
GLUm + NH3m + ATPm -> GLNm + ADPm + Pim 6.3.1.2 22842 KIAA0838
GLN -> GLU + NH3 3.5.1.2 27165 GA GLN -> GLU + NH3 3.5.1.2
2744 GLS GLNm -> GLUm + NH3m 3.5.1.2 440 ASNS ASPm + ATPm + GLNm
-> GLUm + ASNm + AMPm + PPIm 6.3.5.4 1491 CTH LLCT + H2O ->
CYS + HSER 4.4.1.1 OBUT + NH3 <-> HSER 4.4.1.1 2.7 Sulfur
metabolism PATH:hsa00920
9060 PAPSS2, ATPSK2, SK2 APS + ATP -> ADP + PAPS 2.7.1.25 SLF +
ATP -> PPI + APS 2.7.7.4 9061 PAPSS1, ATPSK1, SK1 APS + ATP
-> ADP + PAPS 2.7.1.25 SLF + ATP -> PPI + APS 2.7.7.4 10380
BPNT1 PAP -> AMP + PI 3.1.3.7 6799 SULT1A2 2.8.2.1 6817 SULT1A1,
STP1 2.8.2.1 6818 SULT1A3, STM 2.8.2.1 6822 SULT2A1, STD 2.8.2.2
6783 STE, EST 2.8.2.4 6821 SUOX 1.8.3.1 3. Lipid Metabolism 3.1
Fatty acid biosynthesis (path 1) PATH:hsa00061 2194 FASN 2.3.1.85
3.2 Fatty acid biosynthesis (path 2) PATH:hsa00062 10449 ACAA2,
DSAEC MAACOAm -> ACCOAm + PROPCOAm 2.3.1.16 30 ACAA1, ACAA
MAACOA -> ACCOA + PROPCOA 2.3.1.16 3032 HADHB MAACOA -> ACCOA
+ PROPCOA 2.3.1.16 3.3 Fatty acid metabolism PATH:hsa00071 51
ACOX1, ACOX 1.3.3.6 33 ACADL, LCAD 1.3.99.13 2639 GCDH 1.3.99.7
2179 FACL1, LACS ATP + LCCA + COA <-> AMP + PPI + ACOA
6.2.1.3 2180 FACL2, FACL1, LACS2 ATP + LCCA + COA <-> AMP +
PPI + ACOA 6.2.1.3 2182 FACL4, ACS4 ATP + LCCA + COA <-> AMP
+ PPI + ACOA 6.2.1.3 1374 CPT1A, CPT1, CPT1-L 2.3.1.21 1375 CPT1B,
CPT1-M 2.3.1.21 1376 CPT2, CPT1, CPTASE 2.3.1.21 1632 DCI 5.3.3.8
11283 CYP4F8 1.14.14.1 1543 CYP1A1, CYP1 1.14.14.1 1544 CYP1A2
1.14.14.1 1545 CYP1B1, GLC3A 1.14.14.1 1548 CYP2A6, CYP2A3
1.14.14.1 1549 CYP2A7 1.14.14.1 1551 CYP3A7 1.14.14.1 1553 CYP2A13
1.14.14.1 1554 CYP2B 1.14.14.1 1555 CYP2B6 1.14.14.1 1557 CYP2C19,
CYP2C, P450IIC19 1.14.14.1 1558 CYP2C8 1.14.14.1 1559 CYP2C9,
P450IIC9, CYP2C10 1.14.14.1 1562 CYP2C18, P450IIC17, CYP2C17
1.14.14.1 1565 CYP2D6 1.14.14.1 1571 CYP2E, CYP2E1, P450C2E
1.14.14.1 1572 CYP2F1, CYP2F 1.14.14.1 1573 CYP2J2 1.14.14.1 1575
CYP3A3 1.14.14.1 1576 CYP3A4 1.14.14.1 1577 CYP3A5, PCN3 1.14.14.1
1580 CYP481 1.14.14.1 1588 CYP19, ARO 1.14.14.1 1595 CYP51
1.14.14.1 194 AHHR, AHH 1.14.14.1 3.4 Synthesis and degradation of
ketone bodies PATH:hsa00072 3.5 Sterol biosynthesis PATH:hsa00100
3156 HMGCR MVL + COA + 2 NADP <-> H3MCOA + 2 NADPH 1.1.1.34
4598 MVK, MVLK ATP + MVL -> ADP + PMVL 2.7.1.36 CTP + MVL ->
CDP + PMVL GTP + MVL -> GDP + PMVL UTP + MVL -> UDP + PMVL
10654 PMVK, PMKASE, PMK, HUMPMKI ATP + PMVL -> ADP + PPMVL
2.7.4.2 4597 MVD, MPD ATP + PPMVL -> ADP + PI + IPPP + CO2
4.1.1.33 3422 IDI1 IPPP <-> DMPP 5.3.3.2 2224 FDPS GPP + IPPP
-> FPP + PPI 2.5.1.10 DMPP + IPPP -> GPP + PPI 2.5.1.1 9453
GGPS1, GGPPS DMPP + IPPP -> GPP + PPI 2.5.1.1 GPP + IPPP ->
FPP + PPI 2.5.1.10 2.5.1.29 2222 FDFT1, DGPT 2 FPP + NADPH ->
NADP + SQL 2.5.1.21 6713 SQLE SQL + O2 + NADP -> S23E + NADPH
1.14.99.7 4047 LSS, OSC S23E -> LNST 5.4.99.7 1728 DIA4, NMOR1,
NQO1, NMOR1 1.6.99.2 4835 NMOR2, NQO2 1.6.99.2 37 ACADVL, VLCAD,
LCACD 1.3.99.-- 3.6 Bile acid biosynthesis PATH:hsa00120 1056 CEL,
BSSL, BAL 3.1.1.3 3.1.1.13 3988 LIPA, LAL 3.1.1.13 6646 SOAT1,
ACAT, STAT, SOAT, ACAT1, ACACT 2.3.1.26 1581 CYP7A1, CYP7
1.14.13.17 6715 SRD5A1 1.3.99.5 6716 SRD5A2 1.3.99.5 6718 AKR1D1,
SRD5B1, 3o5bred 1.3.99.6 570 BAAT, BAT 2.3.1.65 3.7 C21-Steroid
hormone metabolism PATH:hsa00140 1583 CYP11A, P450SCC 1.14.15.6
3283 HSD3B1, HSD3B, HSDB3 IMZYMST -> I IMZYMST + CO2 5.3.3.1
IMZYMST -> IIZYMST + CO2 1.1.1.145 3284 HSD382 IMZYMST ->
IIMZYMST + CO2 5.3.3.1 IMZYMST -> IIZYMST + CO2 1.1.1.145 1589
CYP21A2, CYP21, P450C21B, CA21H, CYP21B, P450C21B 1.14.99.10 1586
CYP17, P450C17 1.14.99.9 1584 CYP11B1, P450C11, CYP11B 1.14.15.4
1585 CYP11B2, CYP11B 1.14.15.4 3290 HSD11B1, HSD11, HSD11L, HSD11B
1.1.1.146 3291 HSD1182, HSD11K 1.1.1.146 3.8 Androgen and estrogen
metabolism PATH:hsa00150 3292 HSD17B1, EDH17B2, EDHB17, 1.1.1.62
HSD17 3293 HSD17B3, EDH17B3 1.1.1.62 3294 HSD17B2, EDH17B2 1.1.1.62
3295 HSD17B4 1.1.1.62 3296 HSD17BP1, EDH17B1, EDHB17, 1.1.1.62
HSD17 51478 HSD17B7, PRAP 1.1.1.62 412 STS, ARSC, ARSC1, SSDD
3.1.6.2 414 ARSD 3.1.6.1 415 ARSE, CDPX1, CDPXR, CDPX 3.1.6.1 11185
INMT 2.1.1.-- 24140 JM23 2.1.1.-- 29104 N6AMT1, PRED28 2.1.1.--
29960 FJH1 2.1.1.-- 3276 HRMT1L2, HCP1, PRMT1 2.1.1.-- 51628
LOC51628 2.1.1.-- 54743 HASJ4442 2.1.1.-- 27292 HSA9761 2.1.1.-- 4.
Nucleotide Metabolism 4.1 Purine metabolism PATH:hsa00230 11164
NUDT5, HYSAH1, YSA1H 3.6.1.13 5471 PPAT, GPAT PRPP + GLN -> PPI
+ GLU + PRAM 2.4.2.14 2618 GART, PGFT, PRGS PRAM + ATP + GLY
<-> ADP + PI + GAR 6.3.4.13 FGAM + ATP -> ADP + PI + AIR
6.3.3.1 GAR + FTHF -> THF + FGAR 2.1.2.2 5198 PFAS, FGARAT,
KIAA0361, PURL FGAR + ATP + GLN -> GLU + ADP + PI + FGAM 6.3.5.3
10606 ADE2H1 CAIR + ATP + ASP <-> ADP + PI + SAICAR 6.3.2.6
CAIR <-> AIR + CO2 4.1.1.21 5059 PAICS, AIRC, PAIS CAIR + ATP
+ ASP <-> ADP + PI + SAICAR 6.3.2.6 158 ADSL ASUC <->
FUM + AMP 4.3.2.2 471 ATIC, PURH AICAR + FTHF <-> THF +
PRFICA 2.1.2.3 PRFICA <-> IMP 3.5.4.10 3251 HPRT1, HPRT,
HGPRT HYXAN + PRPP -> PPI + IMP 2.4.2.8 GN + PRPP -> PPI +
GMP 3614 IMPDH1 IMP + NAD -> NAOH + XMP 1.1.1.205 3615 IMPDH2
IMP + NAD -> NAOH + XMP 1.1.1.205 8833 GMPS 6.3.5.2 14923 2987
GUK1 GMP + ATP <-> GDP + ADP 2.7.4.8 DGMP + ATP <->
DGDP + ADP GMP + OATP <-> GDP + DADP 2988 GUK2 GMP + ATP
<-> GDP + ADP 2.7.4.8 DGMP + ATP <-> DGDP + ADP GMP +
DATP <-> GOP + DADP 10621 RPC39 2.7.7.6 10622 RPC32 2.7.7.6
10623 RPC62 2.7.7.6 11128 RPC155 2.7.7.6 25885 DKFZP586M0122
2.7.7.6 30834 2NRD1 2.7.7.6 51082 LOC51082 2.7.7.6 51728 LOC51728
2.7.7.6 5430 POLR2A, RPOL2, POLR2, POLRA 2.7.7.6 5431 POLR2B,
POL2RB 2.7.7.6 5432 POLR2C 2.7.7.6 5433 POLR2D, HSRBP4, HSRPB4
2.7.7.6 5434 POLR2E, RPB5, XAP4 2.7.7.6 5435 POLR2F, RPB6, HRBP14.4
2.7.7.6 5436 POLR2G, RPB7 2.7.7.6 5437 POLR2H, RPB8, RPB17 2.7.7.6
5438 POLR2I 2.7.7.6 5439 POLR2J 2.7.7.6 5440 POLR2K, RP87.0 2.7.7.6
5441 POLR2L, RPB7.6, RPB10 2.7.7.6 5442 POLRMT, APOLMT 2.7.7.6
54479 FLJ10816, Rpo1-2 2.7.7.6 55703 FLJ10388 2.7.7.6 661 BN51T
2.7.7.6 9533 RPA40, RPA39 2.7.7.6 10721 POLQ 2.7.7.7 11232 POLG2,
MTPOLB, HP55, POLB 2.7.7.7 23649 POLR2 2.7.7.7 5422 POLA 2.7.7.7
5423 POLB 2.7.7.7 5424 POLD1, POLD 2.7.7.7 5425 POLD2 2.7.7.7 5426
POLE 2.7.7.7 5427 POLE2 2.7.7.7 5428 POLG 2.7.7.7 5980 REV3L, POLZ,
REV3 2.7.7.7 7498 XDH 1.1.3.22 1.1.1.204 9615 GDA, KIAA1258, CYPIN,
NEDASIN 3.5.4.3 2766 GMPR 1.6.6.8 51292 LOC51292 1.6.6.8 7377 UOX
1.7.3.3 6240 RRM1 ADP + RTHIO -> DADP + OTHIO 1.17.4.1 GDP +
RTHIO -> DGDP + OTHIO COP + RTHIO -> DCDP + OTHIO UDP + RTHIO
-> DUDP + OTHIO 6241 RRM2 ADP + RTHIO -> DADP + OTHIO
1.17.4.1 GDP + RTHIO -> DGDP + OTHIO COP + RTHIO -> DCDP +
OTHIO UDP + RTHIO -> DUDP + OTHIO 4860 NP, PNP AND + PI
<-> AD + R1P 2.4.2.1 GSN + PI <-> GN + R1P DA + PI
<-> AD + R1P DG + PI <-> GN + R1P DIN + PI <->
HYXAN + R1P INS + PI <-> HYXAN + R1P XTSINE + PI <->
XAN + R1P 1890 ECGF1, hPD-ECGF DU + PI <-> URA + DR1P 2.4.2.4
DT + PI <-> THY + DR1P 353 APRT AD + PRPP -> PPI + AMP
2.4.2.7 132 ADK ADN + ATP -> AMP + ADP 2.7.1.20 1633 DCK
2.7.1.74 1716 DGUOK 2.7.1.113 203 AK1 ATP + AMP <-> 2 ADP
2.7.4.3 GTP + AMP <-> ADP + GDP ITP + AMP <-> ADP + IDP
204 AK2 ATP + AMP <-> 2 ADP 2.7.4.3 GTP + AMP <-> ADP +
GOP ITP + AMP <-> ADP + IDP 205 AK3 ATP + AMP <-> 2 ADP
2.7.4.3 GTP + Amp <-> ADP + GOP ITP + AMP <-> ADP + IDP
26289 AK5 ATP + AMP <-> 2 ADP 2.7.4.3 GTP + AMP <-> ADP
+ GDP ITP + AMP <-> ADP + IDP 4830 NME1, NM23, NM23-H1 UDP +
ATP <-> UTP + ADP 2.7.4.6 CDP + ATP <-> CTP + ADP GDP +
ATP <-> GTP + ADP IDP + ATP <-> ITP + IDP DGDP + ATP
<-> DGTP + ADP DUDP + ATP <-> DUTP + ADP DCDP + ATP
<-> DCTP + ADP DTDP + ATP <-> DTTP + ADP DADP + ATP
<-> DATP + ADP 4831 NME2, NM23-H2 UDP + ATP <-> UTP +
ADP 2.7.4.6 CDP + ATP <-> CTP + ADP GDP + ATP <-> GTP +
ADP IDP + ATP <-> ITP + IDP DGDP + ATP <-> DGTP + ADP
DUDP + ATP <-> DUTP + ADP DCDP + ATP <-> DCTP + ADP
DTDP + ATP <-> DTTP + ADP DADP + ATP <-> DATP + ADP
4832 NME3, DR-nm23, DR-NM23 UDP + ATP <-> UTP + ADP 2.7.4.6
CDP + ATP <-> CTP + ADP GDP + ATP <-> GTP + ADP IDP +
ATP <-> ITP + IDP DGDP + ATP <-> DGTP + ADP DUDP + ATP
<-> DUTP + ADP DCDP + ATP <-> DCTP + ADP DTDP + ATP
<-> DTTP + ADP DADP + ATP <-> DATP + ADP 4833 NME4 UDPm
+ ATPm <-> UTPm + ADPm 2.7.4.6
CDPm + ATPm <-> CTPm + ADPm GDPm + ATPm <-> GTPm + ADPm
IDPm + ATPm <-> ITPm + IDPm DGDPm + ATPm <-> DGTPm +
ADPm DUDPm + ATPm <-> DUTPm + ADPm DCDPm + ATPm <->
DCTPm + ADPm DTDPm + ATPm <-> DTTPm + ADPm DADPm + ATPm
<-> DATPm + ADPm 22978 NT5B, PNT5, NT5B-PENDING AMP + H2O
-> PI + ADN 3.1.3.5 GMP -> PI + GSN CMP -> CYTD + PI UMP
-> PI + URI IMP -> PI + INS DUMP -> DU + PI DTMP -> DT
+ PI DAMP -> DA + PI DGMP -> DG + PI DCMP -> DC + PI XMP
-> PI + XTSINE 4877 NT3 AMP -> PI + AON 3.1.3.5 GMP -> PI
+ GSN CMP -> CYTD + PI UMP -> PI + URI IMP -> PI + INS
DUMP -> DU + PI DTMP -> DT + PI DAMP -> DA + PI DGMP ->
DG + PI DCMP -> DC + PI XMP -> PI + XTSINE 4907 NT5, CD73 AMP
-> PI + ADN 3.1.3.5 GMP -> PI + GSN CMP -> CYTD + PI UMP
-> PI + URI IMP -> PI + INS DUMP -> DU + PI DTMP -> DT
+ PI DAMP -> DA + PI DGMP -> DG + PI DCMP -> DC + PI XMP
-> PI + XTSINE 7370 UMPH2 AMP -> PI + ADN 3.1.3.5 GMP ->
PI + GSN CMP -> CYTD + PI UMP -> PI + URI IMP -> PI + INS
DUMP -> DU + PI DTMP -> DT + PI DAMP -> DA + PI DGMP ->
DG + PI DCMP -> DC + PI XMP -> PI + XTSINE 10846 PDE10A cAMP
-> AMP 3.1.4.17 cAMP -> AMP cdAMP -> dAMP cIMP -> IMP
cGMP -> GMP cCMP -> CMP 27115 PDE7B cAMP -> AMP 3.1.4.17
cAMP -> AMP cdAMP -> dAMP cIMP -> IMP cGMP -> GMP cCMP
-> CMP 5136 PDE1A cAMP -> AMP 3.1.4.17 cAMP -> AMP cdAMP
-> dAMP cIMP -> IMP cGMP -> GMP cCMP -> CMP 5137 PDE1C,
HCAM3 cAMP -> AMP 3.1.4.17 cAMP -> AMP cdAMP -> dAMP cIMP
-> IMP cGMP -> GMP cCMP -> CMP 5138 PDE2A cAMP -> AMP
3.1.4.17 cAMP -> AMP cdAMP -> dAMP cIMP -> IMP cGMP ->
GMP cCMP -> CMP 5139 PDE3A, CGI-PDE cAMP -> AMP 3.1.4.17 cAMP
-> AMP cdAMP -> dAMP cIMP -> IMP cGMP -> GMP cCMP ->
CMP 5140 PDE3B cAMP -> AMP 3.1.4.17 cAMP -> AMP cdAMP ->
dAMP cIMP -> IMP cGMP -> GMP cCMP -> CMP 5141 PDE4A, DPDE2
cAMP -> AMP 3.1.4.17 5142 PDE4B, DPDE4, PDEIVB cAMP -> AMP
3.1.4.17 5143 PDE4C, DPDE1 cAMP -> AMP 3.1.4.17 5144 PDE4D,
DPDE3 cAMP -> AMP 3.1.4.17 5145 PDE6A, PDEA, CGPR-A cGMP ->
GMP 3.1.4.17 5146 PDE6C, PDEA2 cGMP -> GMP 3.1.4.17 5147 PDE6D
cGMP -> GMP 3.1.4.17 5148 PDE6G, PDEG cGMP -> GMP 3.1.4.17
5149 PDE6H cGMP -> GMP 3.1.4.17 5152 PDE9A cAMP -> AMP
3.1.4.17 cAMP -> AMP cdAMP -> dAMP cIMP -> IMP cGMP ->
GMP cCMP -> CMP 5153 PDES1B cAMP -> AMP 3.1.4.17 cAMP ->
AMP cdAMP -> dAMP cIMP -> IMP cGMP -> GMP cCMP -> CMP
5158 PDE6B, CSN83, PDEB cGMP -> GMP 3.1.4.17 8654 PDE5A cGMP
-> GMP 3.1.4.17 100 ADA ADN -> INS + NH3 3.5.4.4 DA -> DIN
+ NH3 270 AMPD1, MADA AMP -> IMP + NH3 3.5.4.6 271 AMPD2 AMP
-> IMP + NH3 3.5.4.6 272 AMPD3 AMP -> IMP + NH3 3.5.4.6 953
ENTPD1, CD39 3.6.1.5 3704 ITPA 3.6.1.19 107 ADCY1 ATP -> cAMP +
PPI 4.6.1.1 108 ADCY2, HBAC2 ATP -> cAMP + PPI 4.6.1.1 109
ADCY3, AC3, KIAA0511 ATP -> cAMP + PPI 4.6.1.1 110 ADCY4 ATP
-> cAMP + PPI 4.6.1.1 111 ADCY5 ATP -> cAMP + PPI 4.6.1.1 112
ADCY6 ATP -> CAMP + PPI 4.6.1.1 113 ADCY7, KIAA0037 ATP ->
cAMP + PPI 4.6.1.1 114 ADCY8, ADCY3, HBAC1 ATP -> cAMP + PPI
4.6.1.1 115 ADCY9 ATP -> cAMP + PPI 4.6.1.1 2977 GUCY1A2,
GUC1A2, GC-SA2 4.6.1.2 2982 GUCY1A3, GUC1A3, GUCSA3, GC- 4.6.1.2
SA3 2983 GUCY1B3, GUC1B3, GUCSB3, GC- 4.6.1.2 SBC 2984 GUCY2C,
GUC2C, STAR 4.6.1.2 2986 GUCY2F, GUC2F, GC-F, GUC2DL, 4.6.1.2
RETGC-2 3000 GUCY2D, C0R06, GUC2D, LCA1, 4.6.1.2 GUC1M, LCA, retGC
4881 NPR1, ANPRA, GUC2A, NPRA 4.6.12 4882 NPR2, ANPRB, GUC2B, NPRB,
NPRBi 4.6.1.2 159 ADSS IMP + GTP + ASP -> GDP + PI + ASUC
6.3.4.4 318 NUDT2, APAH1 3.6.1.17 5167 ENPP1, M6S1, LAPPS, PCA1,
PC-1, 3.6.1.9 PDNP1 5168 ENPP2, ATX, PD-IALPHA, PDNP2 3.6.1.9 5169
ENPP3, PD-IBETA, PDNP3 3.6.1.9 3.1.4.1 2272 FHIT 3.6.1.29 4.2
Pyrimidine metabolism PATH:hsa00240 790 CAD GLN + 2 ATP + CO2 ->
GLU + CAP + 2 ADP + PI 6.3.5.5 CAP + ASP -> CAASP + PI 2.1.3.2
CAASP <-> DOROA 3.5.2.3 1723 DHODH DOROA + 02 <-> H202
+ OROA 1.3.3.1 7372 UMPS, OPRT OMP -> CO2 + UMP 4.1.1.23 OROA +
PRPP <-> PPI + OMP 2.4.2.10 51727 LOC51727 ATP + UMP
<-> ADP + UDP 2.7.4.14 CMP + ATP <-> ADP + COP DCMP +
ATP <-> ADP + DCDP 50808 AKL3L 2.7.4.10 1503 CTPS UTP + GLN +
ATP -> GLU + CTP + ADP + PI 6.3.4.2 ATP + UTP + NH3 -> ADP +
PI + CTP 7371 UMPK, TSA903 URI + ATP -> ADP + UMP 2.7.1.48 URI +
GTP -> UMP + GDP CYTD + GTP -> GDP + CMP 7378 UP URI + PI
<-> URA + R1P 2.4.2.3 1806 DPYD, DPD 1.3.1.2 1807 DPYS,
DHPase, DHPASE, DHP 3.5.2.2 51733 LOC51733 3.5.1.6 7296 TXNRD1,
TXNR OTHIO + NADPH -> NADP + RTHIO 1.6.4.5 1854 DUT DUTP ->
PPI + DUMP 3.6.1.23 7298 TYMS, TMS, TS DUMP + METTHF -> DHF +
DTMP 2.1.1.45 978 CDA, CDD CYTD -> URI + NH3 3.5.4.5 DC ->
NH3 + DU 1635 DCTD DCMP <-> DUMP + NH3 3.5.4.12 7083 TK1 DU +
ATP -> DUMP + ADP 2.7.1.21 DT + ATP -> ADP + DTMP 7084 TK2
DUm + ATPm -> DUMPm + ADPm 2.7.1.21 DTm + ATPm -> ADPm +
DTMPm 1841 DTYMK, TYMK, CDC8 DTMP + ATP <-> ADP + DTDP
2.7.4.9 4.3 Nucleotide sugars metabolism PATH:hsa00520 23483 TDPGD
4.2.1.46 1486 CTBS, CTB 3.2.1.-- 5. Amino Acid Metabolism 5.1
Glutamate metabolism PATH:hsa00251 8659 ALDH4, P5CDH P5C + NAD +
H2O -> NADH + GLU 1.5.1.12 2058 EPRS, QARS, QPRS GLU + ATP ->
GTRNA + AMP + PPI 6.1.1.17 6.1.1.15 2673 GFPT1, GFA, GFAT, GFPT F6P
+ GLN -> GLU + GA6P 2.6.1.16 9945 GFPT2, GFAT2 F6P + GLN ->
GLU + GA6P 2.6.1.16 5859 QARS 6.1.1.18 2729 GLCLC, GCS, GLCL CYS +
GLU + ATP -> GC + PI + ADP 6.3.2.2 2730 GLCLR CYS + GLU + ATP
-> GC + PI + ADP 6.3.2.2 2937 GSS, GSHS GLY + GC + ATP -> RGT
+ PI + ADP 6.3.2.3 2936 GSR NADPH + OGT -> NADP + RGT 1.6.4.2
5188 PET112L, PET112 6.3.5.-- 5.2 Alanine and aspartate metabolism
PATH:hsa00252 4677 NARS, ASNRS ATP + ASP + TRNA -> AMP + PPI +
ASPTRNA 6.1.1.22 435 ASL ARGSUCC -> FUM + ARG 4.3.2.1 189 AGXT,
SPAT SERm + PYRm <-> ALAm + 3HPm 2.6.1.51 ALA + GLX <->
PYR + GLY 2.6.1.44 16 AARS 6.1.1.7 1615 DARS 6.1.1.12 445 ASS,
CTLN1, ASS1 CITR + ASP + ATP <-> AMP + PPI + ARGSUCC 6.3.4.5
442 ASPA, ASP, ACY2 3.5.1.15 1384 CRAT, CAT1 2.3.1.7 ACCOA + CAR
-> COA + ACAR 8528 DDO 1.4.3.1 5.3 Glycine, serine and threonine
metabolism PATH:hsa00260 5723 PSPH, PSP 3PSER + H2O -> PI + SER
3.13.3 29968 PSA PHP + GLU <-> AKG + 3PSER 2.6.1.52 OHB + GLU
<-> PHT + AKG 25227 PHGDH, SERA, PGDH, PGD, PGAD 3PG + NAD
<-> NADH + PHP 1.1.1.95 23464 GCAT, KBL 2.3.1.29 211 ALAS1,
ALAS SUCCOA + GLY -> ALAV + COA + CO2 2.3.1.37 212 ALAS2, ANH1,
ASB SUCCOA + GLY -> ALAV + COA + CO2 2.3.1.37 4128 MAOA AMA +
H2O + FAD -> NH3 + FADH2 + MTHGXL 1.4.3.4 4129 MAOB AMA + H2O +
FAD -> NH3 + FADH2 + MTHGXL 1.4.3.4 26 ABP1, AOC1, DAO 1.4.3.6
314 AOC2, DA02, RAO 1.4.3.6 8639 AOC3, VAP-1, VAP1, HPAO 1.4.3.6
2731 GLDC GLY + LIPO <-> SAP + CO2 1.4.4.2 1610 DAO, DAMOX
1.4.3.3 2617 GARS 6.1.1.14 2628 GATM 2.1.4.1 2593 GAMT 2.1.1.2
23761 PISD, PSSC, DKFZP566G2246, PS -> PE + CO2 4.1.1.65
DJ858B16 635 BHMT 2.1.1.5 29958 DMGDH 1.5.99.2 875 CBS SER + HCYS
-> LLCT + H2O 4.2.1.22 6301 SARS, SERS 6.1.1.11 10993 SDS, SDH
SER -> PYR + NH3 + H2O 4.2.1.13 6897 TARS 6.1.1.3 5.4 Methionine
metabolism PATH:hsa00271 4143 MAT1A, MATA1, SAMS1, MAT, SAMS MET +
ATP + H2O -> PPI + PI + SAM 2.5.1.6 4144 MAT2A, MATA2, SAMS2,
MATII MET + ATP + H2O -> PPI + PI + SAM 2.5.1.6 1786 DNMT1,
MCMT, DNMT SAM + DNA -> SAH + DNA5MC 2.1.1.37 10768 AHCYL1,
XPVKONA SAH + H2O -> HCYS + AON 3.3.1.1 191 AHCY, SAHH SAH + H2O
-> HCYS + AON 3.3.1.1 4141 MARS, METRS, MTRNS 6.1.1.10 4548 MTR
HCYS + MTHF -> THF + MET 2.1.1.13
5.5 Cysteine metabolism PATH:hsa00272 833 CARS 6.1.1.16 1036 CDO1
CYS + O2 <-> CYSS 1.13.11.20 8509 NDST2, HSST2, NST2 2.8.2.--
5.6 Valine, leucine and isoleucine degradation PATH:hsa00280 586
BCAT1, BCT1, ECA39, MECA39 AKG + ILE -> OMVAL + GLU 2.6.1.42 AKG
+ VAL -> OIVAL + GLU AKG + LEU -> OICAP + GLU 587 BCAT2, BCT2
OICAPm + GLUm <-> AKGm + LEUm 2.6.1.42 OMVALm + GLUm
<-> AKGm + ILEm 5014 OVD1A 1.2.4.4 593 BCKDHA, MSUD1 OMVALm +
COAm + NADm -> MBCOAm + NADHm + CO2m 1.2.4.4 OIVALm + COAm +
NADm -> IBCOAm + NADHm + CO2m OICAPm + COAm + NADm -> IVCOAm
+ NADHm + CO2m 594 BCKDHB, E1B OMVALm + COAm + NADm -> MBCOAm +
NADHm + CO2m 1.2.4.4 OIVALm + COAm + NADm -> IBCOAm + NADHm +
CO2m OICAPm + COAm + NADH -> IVCOAm + NADHm + CO2m 3712 IVD
IVCOAm + FADm -> MCRCOAm + FADH2m 1.3.99.10 316 AOX1, AO 1.2.3.1
4164 MCCC1 MCRCOAm + ATPm + CO2m + H2Om -> MGCOAm + ADPm +
6.4.1.4 Pim 4165 MCCC2 MCRCOAm + ATPm + CO2m + H2Om -> MGCOAm +
ADPm + 6.4.1.4 Pim 5.7 Valine, leucine and Isoleucine biosynthesis
PATH:hsa00290 23395 KIAA0028, LARS2 6.4.1.4 3926 LARS 6.4.1.4 3376
IARS, ILRS 61.1.5 7406 VARS1, VARS 6.1.1.9 7407 VARS2, G7A 6.1.1.9
5.8 Lysine biosynthesis PATH:hsa00300 3735 KARS, KIAA0070 ATP + LYS
+ LTRNA -> AMP + PPI + LLTRNA 6.1.1.6 5.9 Lysine degradation
PATH:hsa00310 8424 BBOX, BBH, GAMMA-BBH, G-BBH 1.14.11.1 5351 PLOD,
LLH 1.14.11.4 5352 PLOD2 1.14.11.4 8985 PLOD3, LH3 1.14.11.4 10157
LKR/SDH, AASS LYS + NADPH + AKG -> NADP + H2O + SAC 1.5.1.9 SAC
+ H2O + NAD -> GLU + NADH + AASA 5.10 Arginine and proline
metabolism PATH:hsa00330 5009 OTC ORNm + CAPm -> CITRm + Pim +
Hm 2.1.3.3 383 ARG1 ARG -> ORN + UREA 3.5.3.1 384 ARG2 ARG ->
ORN + UREA 3.5.3.1 4842 NOS1, NOS 1.14.13.39 4843 NOS2A, NOS2
1.14.13.39 4846 NOS3, ECHOS 1.14.13.39 4942 OAT ORN + AKG <->
GLUGSAL + GLU 2.6.1.13 5831 PYCR1, P5C, PYCR P5C + NADPH -> PRO
+ NADP 1.5.1.2 P5C + NADH -> PRO + NAD PHC + NADPH -> HPRO +
NADP PHC + NADH -> HPRO + NAD 5033 P4HA1, P4HA 1.14.11.2 5917
RARS ATP + ARG + ATRNA -> AMP + PPI + ALTRNA 6.1.1.19 1152 CKB,
CKBB PCRE + ADP -> CRE + ATP 2.7.3.2 1156 CKBE 2.7.3.2 1158 CKM,
CKMM 2.7.3.2 1159 CKMT1, CKMT, UMTCK 2.7.3.2 1160 CKMT2, SMTCK
2.7.3.2 6723 SRM, SPS1, SRML1 PTRSC + SAM -> SPRMD + 5MTA
2.5.1.16 262 AMD1, ADOMETDC SAM <-> DSAM + CO2 4.1.1.50 263
AMDP1, AMD, AMD2 SAM <-> DSAM + CO2 4.1.1.50 1725 DHPS SPRMD
+ Qm -> DAPRP + QH2m 1.5.99.6 6611 SMS DSAM + SPRMD -> 5MTA +
SPRM 2.5.1.22 4953 ODC1 ORN -> PTRSC + CO2 4.1.1.17 6303 SAT,
SSAT 2.3.1.57 5.11 Histidine metabolism PATH:hsa00340 10841 FTCD
FIGLU + THF -> NFTHF + GLU 2.1.2.5 4.3.1.4 3067 HDC 4.1.1.22
1644 DDC, AADC 4.1.1.28 3176 HNMT 2.1.1.8 218 ALDH3 ACAL + NAD
-> NADH + AC 1.2.1.5 220 ALDH6 ACAL + NAD -> NADH + AC
1.2.1.5 221 ALDH7, ALDH4 ACAL + NAD -> NADH + AC 1.2.1.5 222
ALDH8 ACAL + NAD -> NADH + AC 1.2.1.5 3035 HARS ATP + HIS +
HTRNA -> AMP + PPI + HHTRNA 6.1.1.21 5.12 Tyrosine metabolism
PATH:hsa00350 6898 TAT AKG + TYR -> HPHPYR + GLU 2.6.1.5 3242
HPD, PPD HPHPYR + O2 -> HGTS + CO2 1.13.11.27 3081 HGD, AKU, HGO
HGTS + O2 -> MACA 1.13.11.5 2954 GSTZI, MAAI MACA -> FACA
5.2.1.2 2.5.1.18 2184 FAH FACA + H2O -> FUM + ACA 3.7.1.2 7299
TYR, OCA1A 1.14.18.1 7054 TH, TYH 1.14.16.2 1621 DBH 1.14.17.1 5409
PNMT, PENT 2.1.1.28 1312 COMT 2.1.1.6 7173 TPO, TPX 1.11.1.8 5.13
Phenylalanine metabolism PATH:hsa00360 501 ATQ1 1.2.1.-- 5.14
Tryptophan metabolism PATH:hsa00380 6999 TDO2, TPH2, TRPO, TDO TRP
+ O2 -> FKYN 1.13.11.11 8564 KMO KYN + NAD PH + O2 -> HKYN +
NADP + H2O 1.14.13.9 8942 KYNU KYN -> ALA + AN 3.7.1.3 HKYN +
H2O -> HAN + ALA 23498 HAAO, HAO, 3-HAO HAN + O2 -> CMUSA
1.13.11.6 7166 TPH, TPRH 1.14.16.4 438 ASMT, HIOMT, ASMTY 2.1.1.4
15 AANAT, SNAT 2.3.1.87 3620 INDO, IDO 1.13.11.42 10352 WARS2 ATPm
+ TRPm + TRNAm -> AMPm + PPIm + TRPTRNAm 6.1.1.2 7453 WARS,
IFP53, IF153, GAMMA-2 ATP + TRP + TRNA -> AMP + PPI + TRPTRNA
6.1.1.2 4734 NEDD4, KIAA0093 6.3.2.-- 5.15 Phenylalanine, tyrosine
and tryptophan biosynthesis PATH:hsa00400 5053 PAH, PKU1 PHE + THBP
+ O2 -> TYR + DHBP + H2O 1.14.16.1 10667 FARS1 6.1.1.20 2193
FARSl, CML33 6.1.1.20 10056 PheHB 6.1.1.20 8565 YARS, TYRRS, YTS,
YRS 6.1.1.1 5.16 Urea cycle and metabolism of amino groups
PATH:hsa00220 5832 PYCS 2.7.2.11 GLUP + NADH -> NAD + PI +
GLUGSAL 1.2.1.41 GLUP + NADPH -> NADP + PI + GLUGSAL 95 ACY1
3.5.1.14 6. Metabolism of Other Amino Acids 6.1 beta-Alanine
metabolism PATH:hsa00410 6.2 Taurine and hypotaurine metabolism
PATH:hsa00430 2678 GGT1, GTG, D22S672, D22S732, RGT + ALA ->
CGLY + ALAGLY 2.3.2.2 GGT 2679 GGT2, GGT RGT + ALA -> CGLY +
ALAGLY 2.3.2.2 2680 GGT3 RGT + ALA -> CGLY + ALAGLY 2.3.2.2 2687
GGTLA1, GGT-REL, DKFZP5660011 RGT + ALA -> CGLY + ALAGLY 2.3.2.2
6.3 Aminophosphonate metabolism PATH:hsa00440 5130 PCYT1A, CTPCT,
CT, PCYT1 PCHO + CTP -> CDPCHO + PPI 2.7.7.15 9791 PTDSS1,
KIAA0024, PSSA CDPDG + SER <-> CMP + PS 2.7.8.-- 6.4
Selenoamino acid metabolism PATH:hsa00450 22928 SPS2 2.7.9.3 22929
SPS, SELD 2.7.9.3 6.5 Cyanoamino acid metabolism PATH:hsa00460 6.6
D-Glutamine and D-glutamate metabolism PATH:hsa00471 6.7 D-Arginine
and D-ornithine metabolism PATH:hsa00472 6.9 Glutathione metabolism
PATH:hsa00480 5162 PEPB 3.4.11.4 2655 GCTG 2.3.2.4 2876 GPX1,
GSHPX1 2 RGT + H2O2 <-> OGT 1.11.1.9 2877 GPX2, GSHPX-GI 2
RGT + H2O2 <-> OGT 1.11.1.9 2878 GPX3 2 RGT + H2O2 <->
OGT 1.11.1.9 2879 GPX4 2 RGT + H2O2 <-> OGT 1.11.1.9 2880
GPX5 2 RGT + H2O2 <-> OGT 1.11.1.9 2881 GPX6 2 RGT + H2O2
<-> OGT 1.11.1.9 2938 GSTA1 2.5.1.18 2939 GSTA2, GST2
2.5.1.18 2940 GSTA3 2.5.1.18 2941 GSTA4 2.5.1.18 2944 GSTM1, GST1,
MU 2.5.1.18 2946 GSTM2, GST4 2.5.1.18 2947 GSTM3, GST5 2.5.1.18
2948 GSTM4 2.5.1.18 2949 GSTM5 2.5.1.18 2950 GSTP1, FAEES3, DFN7,
GST3, PI 2.5.1.18 2952 GSTT1 2.5.1.18 2953 GSTT2 2.5.1.18 4257
MGST1, GST12, MGST, MGST-I 2.5.1.18 4258 MGST2, GST2, MGST-II
2.5.1.18 4259 MGST3, GST-III 2.5.1.18 7. Metabolism of Complex
Carbohydrates 7.1 Starch and sucrose metabolism PATH:hsa00500 6476
SI 3.2.1.10 3.2.1.48 11181 TREH, TRE, TREA TRE -> 2 GLC 3.2.1.28
2990 GUSB 3.2.1.31 2632 GBE1 GLYCOGEN + PI -> G1P 2.4.1.18 5834
PYGB GLYCOGEN + PI -> G1P 2.4.1.1 5836 PYGL GLYCOGEN + PI ->
G1P 2.4.1.1 5837 PYGM GLYCOGEN + PI -> G1P 2.4.1.1 2997 GYS1,
GYS UDPG -> UDP + GLYCOGEN 2.4.1.11 2998 GYS2 UDPG -> UDP +
GLYCOGEN 2.4.1.11 276 AMY1A, AMY1 3.2.1.1 277 AMY1B, AMY1 3.2.1.1
278 AMY1C, AMY1 3.2.1.1 279 AMY2A, AMY2 3.2.1.1 280 AMY2B, AMY2
3.2.1.1 178 AGL, GDE 2.4.1.25 3.2.1.33 10000 AKT3, PKBG, RAC-GAMMA,
PRKBG 2.7.1.-- 1017 CDK2 1018 CDK3 2.7.1.-- 1019 CDK4, PSK-J3
2.7.1.-- 1020 CDK5, PSSALRE 2.7.1.-- 1021 CDK8, PLSTIRE 2.7.1.--
1022 CDK7, CAK1, STK1, CDKN7 2.7.1.-- 1024 CDK8, K35 2.7.1.-- 1025
CDK9, PITALRE, CDC2L4 2.7.1.-- 10298 PAK4 2.7.1.-- 10746 MAP3K2,
MEKK2 2.7.1.-- 1111 CHEK1, CHK1 2.7.1.-- 11200 RAD53, CHK2, CDS1,
HUCDS1 2.7.1.-- 1195 CLK1, CLK 2.7.1.-- 1326 MAP3K8, COT, EST,
ESTF, TPL-2 2.7.1.-- 1432 MAPK14, CSBP2, CSPB1, PRKM14, PRKM15,
CSBP1, P38, MXI2 2.7.1.-- 1452 CSNK1A1 2.7.1.-- 1453 CSNK1D, HCKID
2.7.1.-- 1454 CSNK1E, HCKIE 2.7.1.-- 1455 CSNK1G2 2.7.1.-- 1456
CSNK1G3 2.7.1.-- 1612 DAPK1, DAPK 2.7.1.-- 1760 DMPK, DM, DMK, DM1
2.7.1.-- 1859 DYRK1A, DYRK1, DYRK, MNB, MNBH 2.7.1.-- 208 AKT2,
RAC-BETA, PRKBB, PKBBETA 2.7.1.-- 269 AMHR2, AMHR 2.7.1.-- 27330
RPS6KA6, RSK4 2.7.1.-- 2868 GPRK2L, GPRK4 2.7.1.-- 2869 GPRK5, GRK5
2.7.1.-- 2870 GPRK6, GRK6 2.7.1.-- 29904 HSU93850 2.7.1.-- 30811
HUNK 2.7.1.-- 3611 ILK, P59 2.7.1.-- 3654 IRAK1, IRAK 2.7.1.-- 369
ARAF1, PKS2, RAFA1 2.7.1.-- 370 ARAF2P, PKS1, ARAF2 2.7.1.-- 3984
LIMK1, LIMK 2.7.1.-- 3985 LIMK2 2.7.1.-- 4117 MAK 2.7.1.-- 4140
MARK3, KP78 2.7.1.-- 4215 MAP3K3, MAPKKK3, MEKK3 2.7.1.-- 4216
MAP3K4, MAPKKK4, MTK1, MEKK4, KIAA0213 2.7.1.-- 4217 MAP3K5, ASK1,
MAPKKK5, MEKK5 2.7.1.-- 4293 MAP3K9, PRKE1, MLK1 2.7.1.-- 4294
MAP3K10, MLK2, MST 2.7.1.-- 4342 MOS 2.7.1.-- 4751 NEK2, NLK1
2.7.1.-- 4752 NEK3 2.7.1.-- 5058 PAK1, PAKalpha 5062 PAK2, PAK65,
PAKgamma 2.7.1.-- 5063 PAK3, MRX30, PAK3beta 2.7.1.-- 5127 PCTK1,
PCTGAIRE 2.7.1.-- 5128 PCTK2 2.7.1.-- 5129 PCTK3, PCTAIRE 2.7.1.--
5292 PIM1, PIM 2.7.1.-- 5347 PLK, PLK1 2.7.1.-- 5562 PRKAA1
2.7.1.-- 5563 PRKAA2, AMPK, PRKAA 2.7.1.-- 5578 PRKCA, PKCA
2.7.1.-- 5579 PRKCB1, PKCB, PRKCB, PRKCB2 2.7.1.-- 5580 PRKCD
2.7.1.-- 5581 PRKCE 2.7.1.-- 5582 PRKCG, PKCC, PKCG 2.7.1.-- 5583
PRKCH, PKC-L, PRKCL 2.7.1.-- 5584 PRKCl, DXS1179E, PKCl 2.7.1.--
5585 PRKCL1, PAK1, PRK1, DBK, PKN 2.7.1.-- 5586 PRKCL2, PRK2
2.7.1.-- 5588 PRKCQ 2.7.1.-- 5590 PRKCZ 2.7.1.-- 5594 MAPK1, PRKM1,
P41MAPK, P42MAPK, ERK2, ERK, MAPK2, 2.7.1.-- PRKM2 5595 MAPK3,
ERK1, PRKM3, P44ERK1,
P44MAPK 2.7.1.-- 5597 MAPK6, PRKM6, P97MAPK, ERK3 2.7.1.-- 5598
MAPK7, BMKI, ERK5, PRKM7 2.7.1.-- 5599 MAPK8, JNK, JNK1, SAPK1,
PRKM8, 2.7.1.-- JNK1A2 5601 MAPK9, JNK2, PRKM9, P54ASAPK, 2.7.1.--
JUNKINASE 5602 MAPK10, JNK3, PRKM10, P493F12, P54BSAPK 2.7.1.--
5603 MAPK13, SAPK4, PRKM13, P38DELTA 2.7.1.-- 5604 MAP2K1, MAPKK1,
MEK1, MKK1, PRKMK1 2.7.1.-- 5605 MAP2K2, MEK2, PRKMK2 2.7.1.-- 5606
MAP2K3, MEK3, MKK3, PRKMK3 2.7.1.-- 5607 MAP2K5, MEK5, PRKMK5
2.7.1.-- 5608 MAP2K6, MEK6, MKK6, SAPKK3, PRKMK6 2.7.1.-- 5609
MAP2K7, MAPKK7, MKK7, PRKMK7, JNKK2 2.7.1.-- 5610 PRKR, EIF2AK1,
PKR 2.7.1.-- 5613 PRKX, PKX1 2.7.1.-- 5894 RAF1 2.7.1.-- 613 BCR
CMl, PHL, BCR1 D22S11, D22S662 2.7.1.-- 6195 RPS6KA1, HU-1, RSK,
RSK1, MAPKAPK1A 2.7.1.-- 6196 RPS6KA2, HU-2, MAPKAPKIC, RSK, RSK3
2.7.1.-- 6197 RPS6KA3, RSK2, HU-2, HU-3, RSK, 2.7.1.-- MAPKAPK1B,
ISPK-1 6198 RPS6KB1, STK14A 2.7.1.-- 6199 RPS6KB2, P70-BETA,
P70S6KB 2.7.1.-- 6300 MAPK12, ERK6, PRKM12, SAPK3, P38GAMMA, SAPK-3
2.7.1.-- 6416 MAP2K4, JNKK1, MEK4, PRKMK4, SERK1, MKK4 2.7.1.--
6446 SGK 2.7.1.-- 658 BMPR1B, ALK-6, ALK6 2.7.1.-- 659 BMPR2,
BMPR-II, BMPR3, BRK-3 2.7.1.-- 673 BRAF 2.7.1.-- 6792 STK9 2.7.1.--
6794 STK11, LKB1, PJS 2.7.1.-- 6885 MAP3K7, TAK1 2.7.1.-- 699 BUB1
2.7.1.-- 701 BUB1B, BUBR1, MAD3L 2.7.1.-- 7016 TESK1 2.7.1.-- 7272
TTK, MPS1L1 2.7.1.-- 7867 MAPKAPK3, 3PK, MAPKAP3 2.7.1.-- 8408 ULK1
2.7.1.-- 8558 CDK10, PISSLRE 2.7.1.-- 8621 CDC2L5, CDC2L, CHED
2.7.1.-- 8737 RIPK1, RIP 2.7.1.-- 8814 CDKL1, KKIALRE 2.7.1.-- 8899
PRP4, PR4H 2.7.1.-- 9064 MAP3K6, MAPKKK6 2.7.1.-- 9149 DYRK1B
2.7.1.-- 92 ACVR2, ACTRII 2.7.1.-- 9201 DCAMKL1, KIAA0369 2.7.1.--
93 ACVR2B 2.7.1.-- 983 CDC2 2.7.1.-- 984 CDC2L1 2.7.1.-- 5205 FIC1,
BRIC, PFIC1, PFIC, ATP8B1 3.6.1.-- DHPP -> DHP + PI GTP ->
GSN + 3 PI DGTP -> DG + 3 PI 7.2 Glycoprotein biosynthesis
PATH:hsa00510 1798 DPAGT1, DPAGt, UGAT, UAGT, 2.7.8.15 D11S366,
DGPT, DPAGT2, GPT 29880 ALG5 2.4.1.117 8813 DPM1 GDPMAN + DOLP
-> GDP + DOLMANP 2.4.1.83 1650 DDOST, OST, OST48, KIAA0115
2.4.1.119 6184 RPN1 2.4.1.119 6185 RPN2 2.4.1.119 10130 P5 5.3.4.1
10954 PDIR 3.3.4.1 11008 PDI 5.3.4.1 2923 GRP58, ERp57, ERp60,
ERp61, 5.3.4.1 GRP57, P58, PI-PLC, ERP57, ERP60, ERP61 5034 P4HB,
PROHB, P04DB, ERBA2L 5.3.4.1 7841 GCS1 3.2.1.106 4121 MAN1A1, MAN9,
HUMM9 3.2.1.113 4245 MGAT1, GLYT1, GLCNAC-TI, GNT-I, 2.4.1.101 MGAT
4122 MAN2A2, MANA2X 3.2.1.114 4124 MAN2A1, MANA2 3.2.1.114 4247
MGAT2, CDGS2, GNT-II, GLCNACTII, 2.4.1.143 GNT2 4248 MGAT3, GNT-III
2.4.1.144 6487 SIAT6, ST3GALII 2.4.99.6 6480 SIAT1 2.4.99.1 2339
FNTA, FPTA, PGGT1A 2.5.1.-- 2342 FNTB, FPTB 2.5.1.-- 5229 PGGT1B,
BGGI, GGTI 2.5.1.-- 5875 RABGGTA 2.5.1.-- 5876 RABGGTB 2.5.1.--
1352 COX10 2.5.1.-- 7.3 Glycoprotein degradation PATH:hsa00511 4758
NEU1, NEU 3.2.1.18 3073 HEXA, TSD 3.2.1.52 3074 HEXB 3.2.1.52 4123
MAN2C1, MANA, MANA1, MAN6A8 3.2.1.24 4125 MAN2B1, MANS, LAMAN
3.2.1.24 4126 MANBA, MANB1 3.2.1.25 2517 FUCA1 3.2.1.51 2519 FUCA2
3.2.1.51 175 AGA, AGU 3.5.1.26 7.4 Aminosugars metabolism
PATH:hsa00530 6675 UAP1, SPAG2, AGX1 UTP + NAGAIP <-> UDPNAG
+ PPI 2.7.7.23 10020 GNE, GLCNE 5.1.3.14 22951 CMAS 2.7.7.43 1727
DIA1 1.6.2.2 4669 NAGLU, NAG 3.2.1.50 7.5 Lipopolysaccharide
biosynthesis PATH:hsa00540 6485 SIAT5, SAT3, STZ 2,4.99.-- 7903
SIAT8D, PST, PST1, ST8SIA-IV 2.4.99.-- 8128 SIAT8B, STX, STSSIA-II
2.4.99.-- 7.7 Glycosaminoglycan degradation PATH:hsa00531 3423 IDS,
MPS2, SIDS 3.1.6.13 3425 IDUA, IDA 3.2.1.76 411 ARSB 3.1.6.12 2799
GNS, G6S 3.1.6.14 2588 GALNS, MPS4A, GALNAC6S, GAS 3.1.6.4 8.
Metabolism of Complex Lipids 8.1 Glycerolipid metabolism
PATH:hsa00561 10554 AGPAT1, LPAAT-ALPHA, G15 AGL3P + 0.017 C100ACP
+ 0.062 C120ACP + 0.100 C140ACP + 2.3.1.51 0.270 C160ACP + 0.169
C161ACP + 0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> PA +
ACP 10555 AGPAT2, LPAAT-BETA AGL3P + 0.017 C100ACP + 0.062 C120ACP
+ 0.100 C140ACP + 2.3.1.51 0.270 C160ACP + 0.169 C161ACP + 0.055
C180ACP + 0.235 C181ACP + 0.093 C182ACP -> PA + ACP 1606 DGKA,
OAGK, DAGK1 2.7.1.107 1608 DGKG, DAGK3 2.7.1.107 1609 DGKQ, DAGK4
2.7.1.107 8525 DGKZ, DAGK5, HDGKZETA 2.7.1.107 8526 DGKE, DAGK6,
DGK 2.7.1.107 8527 DGKD, DGKDELTA, KIAA0145 2.7.1.107 1120 CHKL ATP
+ CHO -> ADP + PCHO 2.7.1.32 EKI1 ATP + ETHM -> ADP + PETHM
2.7.1.82 1119 CHK, CKI ATP + CHO -> ADP + PC HO 2.7.1.32 43
ACHE, YT 3.1.1.7 1103 CHAT 2.3.1.6 5337 PLD1 3.1.4.4 26279 PLA2G2D,
SPLA2S 3.1.1.4 30814 PLA2G2E 3.1.1.4 5319 PLA2G1B, PIA2, PLA2A,
PPLA2 3.1.1.4 5320 PLA2G2A, MOM1, PLA2B, PLA2L 3.1.1.4 5322 PLA2G5
3.1.1.4 8398 PLA2G6, IPLA2 3.1.1.4 8399 PLA2G10, SPLA2 3.1.1.4 1040
CDS1 PA + CTP <-> CDPDG + PPI 2.7.7.41 10423 PIS CDPDG + MYOI
-> CMP + PINS 2.7.8.41 2710 GK GL + ATP -> GL3P + ADP
2.7.1.30 2820 GPD2 GL3Pm + FADm -> T3P2m + FADH2m 1.1.99.5 2819
GPD1 T3P2 + NADH <-> GL3P + NAD 1.1.1.8 248 ALPI AHTD ->
DHP + 3 PI 3.1.3.1 249 ALPL, HOPS, TNSALP AHTD -> DHP + 3 PI
3.1.3.1 250 ALPP AHTD -> DHP + 3 PI 3.1.3.1 251 ALPPL2 AHTD
-> DHP + 3 PI 3.1.3.1 439 ASNA1, ARSA-I 3.6.1.16 8694 DGAT,
ARGP1 DAGLY + 0.017 C100ACP + 0.062 C120ACP + 0.100 C140ACP +
2.3.1.20 0.270 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235
C181ACP + 0.093 C182ACP -> TAGLY + ACP 3989 LIPB 3.1.1.3 3990
LIPC, HL 3.1.1.3 5406 PNLIP 3.1.1.3 5407 PNLIPRP1, PLRP1 3.1.1.3
5408 PNLIPRP2, PLRP2 3.1.1.3 8513 LIPF, HGL, HLAL 3.1.1.3 4023 LPL,
LIPD 3.1.1.34 8443 GNPAT, DHAPAT, DAP-AT 2.3.1.42 8540 AGP-S, ADAS,
ADHAPS, ADPS, 2.5.1.26 ALDHPSY 4186 MDCR, MDS, US1 3.1.1.47 5048
PAFAH1B1, US1, MDCR, PAFAH 3.1.1.47 5049 PAFAH 1B2 3.1.1.47 5050
PAFAH1B3 3.1.1.47 5051 PAFAH2, HSD-PLA2 3.1.1.47 7941 PLA2G7,
PAFAH, LDL-PLA2 3.1.1.47 8.2 Inositol phosphate metabolism
PATH:hsa00562 5290 PIK3CA ATP + PINS -> ADP + PINSP 2.7.1.137
5291 PIK3CB, PIK3C1 ATP + PINS -> ADP + PINSP 2.7.1.137 5293
PIK3CD ATP + PINS -> ADP + PINSP 2.7.1.137 5294 PIK3CG ATP +
PINS -> ADP + PINSP 2.7.1.137 5297 PIK4CA, PI4K-ALPHA ATP + PINS
-> ADP + PINS4P 2.7.1.67 5305 PIP5K2A PINS4P + ATP -> D45P1 +
ADP 2.7.1.68 5330 PLCB2 D45P1-> TPI + DAGLY 3.1.4.11 5331 PLCB3
D45P1-> TPI + DAGLY 3.1.4.11 5333 PLCD1 D45PI -> TPI + DAGLY
3.1.4.11 5335 PLCG1, PLC1 D45PI -> TPI + DAGLY 3.1.4.11 5336
PLCG2 D45PI -> TPI + DAGLY 3.1.4.11 3612 IMPA1, IMPA MI1P ->
MYO1+ PI 3.1.3.25 3613 IMPA2 MI1P -> MYOI + PI 3.1.3.25 3628
INPP1 3.1.3.57 3632 INPP5A 3633 INPP5B 3.1.3.56 3636 INPPL1, SHIP2
3.1.3.56 4952 OCRL, LOCR, OCRL1, INPP5F 3.1.3.56 8867 SYNJ1, INPP5G
3.1.3.56 3706 ITPKA 2.7.1.127 51477 ISYNA1 G6P -> MI1P 5.5.1.4
3631 INPP4A, INPP4 3.1.3.66 6821 INPP4B 3.1.3.66 8.3
Sphingophospholipid biosynthesis PATH:hsa00570 6609 SMPD1, NPD
3.1.4.12 8.4 Phospholipid degradation PATH:hs800580 1178 CLC 3.1.15
5321 PLA2G4A, CPLA2-ALPHA, PLA2G4 3.1.1.5 8.5 Sphingogtycolipid
metabolism PATH:hsa00600 10558 SPTLC1, LCB1, SPTI PALCOA + SER
-> COA + DHSPH + CO2 2.3.1.50 9517 SPTLC2, KIAA0526, LCB2 PALCOA
+ SER -> COA + DHSPH + CO2 2.3.1.50 427 ASAH, AC, PHP32 3.5.1.23
7357 UGCG, GCS 2.4.1.80 2629 GBA, GLUC 3.2.1.45 2583 GALGT, GALNACT
2.4.1.92 6489 SIAT8A SIAT8, ST8SIA-I 2.4.99.8 6481 SIAT2 2.4.99.2
4668 NAGA, D22S674, GALB 3.2.1.49 9514 CST 2.8.2.11 410 ARSA, MLD
3.1.6.8 8.6 Blood group glycolipid biosynthesis - lact series PATH
hsa00601 28 ABO 2.4.1.40 2.4.1.37 2525 FUT3, LE 2.4.1.65 2527 FUT5,
FUC-TV 2.4.1.65 2528 FUT6 2.4.1.65 2523 FUT1, H, HH 2.4.1.69 2524
FUT2, SE 2.4.1.69 8.7 Blood group glycolipid biosynthesis - neolact
series PATH:hsa00602 2651 GCNT2, IGNT, NACGT1, NAGCT1 2.4.1.1.50
8.8 Prostaglandin and leukotriene metabolism PATH:hsa00590 239
ALOX12, LOG12 1.13.11.31 246 ALOX15 1.13.11.33 240 ALOX5 1.13.11.34
4056 LTC4S 2.5.1.37 4048 LTA4H 3.3.2.6 4051 CYP4F3, CYP4F, LTB4H
1.14.13.30 8529 CYP4F2 1.14.13.30 5742 PTGS1, PGHS-1 1.14.99.1 5743
PTGS2, COX-2, COX2 1.14.99.1 27306 PGDS 5.3.99.2 5730 PTGDS
5.3.99.2 5740 PTGIS, CYP8, PGIS 5.3.99.4 6916 TBXAS1, CYP5 5.3.99.5
873 CBR1, CBR 1.1.1.184 1.1.1.189 1.1.1.197
874 CBR3 1.1.1.184 9. Metabolism of Cofactors and Vitamins 9.2
Riboflavin metabolism PATH:hsa00740 52 ACP1 3.1.3.48 FMN ->
RIBOFLAV + PI 3.1.3.2 53 ACP2 FMN -> RIBOFLAV + PI 3.1.3.2 54
ACP5, TRAP FMN -> RIBOFLAV + PI 3.1.3.2 55 ACPP, PAP FMN ->
RIBOFLAV + PI 3.1.3.2 9.3 Vitamin B6 metabolism PATH:hsa00750 8566
PDXK, PKH, PNK PYRDX + ATP -> P5P + ADP 2.7.1.35 PDLA + ATP
-> POLA5P + ADP PL + ATP -> PL5P + ADP 9.4 Nicotinate and
nicotinamide metabolism PATH:hsa00760 23475 QPRT QA + PRPP ->
NAMN + CO2 + PPI 2.4.2.19 4837 NNMT 2.1.1.1 683 BST1, CD157 NAD
-> NAM + ADPRIB 3.2.2.5 952 CD38 NAD -> NAM + ADPRIB 3.2.2.5
23530 NNT 1.6.1.2 9.5 Pantothenate and CoA biosynthesis
PATH:hsa00770 9.6 Biotin metabolism PATH:hsa00780 3141 HLCS, HCS
6.3.4.-- 6.3.4.9 6.3.4.10 6.3.4.11 6.3.4.15 3.5.1.12 686 BTD 9.7
Folate biosynthesis PATH:hsa00790 2643 GCH1, DYT5, GCH, GTPCH1 GTP
-> FOR + AHTD 3.5.4.16 1719 DHFR DHF + NADPH -> NADP + THF
1.5.1.3 2356 FPGS THF + ATP + GLU <-> ADP + PI + THFG
6.3.2.17 8836 GGH, GH 3.4.19.9 5805 PTS 4.6.1.10 6697 SPR 1.1.1.153
5860 QDPR, DHPR, PKU2 NADPH + DHBP -> NADP + THBP 1.6.99.7 9.8
One carbon pool by folate PATH:hsa00670 10840 FTHFD 1.5.1.6 10588
MTHFS ATP + FTHF -> ADP + PI + MTHF 6.3.3.2 9.10 Porphyrin and
chlorophyll metabolism PATH:hsa00860 210 ALAD 2 ALAV -> PBG
4.2.1.24 3145 HMBS, PBGD, UPS 4 PBG -> HMB + 4 NH3 4.3.1.8 7390
UROS HMB -> UPRG 4.2.1.75 7389 UROD UPRG -> 4 CO2 + CPP
4.1.1.37 1371 CPO, CPX O2 + CPP -> 2 CO2 + PPHG 1.3.3.3 5498
PPOX, PPO O2 + PPHGm -> PPIXm 1.3.3.4 2235 FECH, FCE PPIXm ->
PTHm 4.99.1.1 3162 HMOX1, HO-1 1.14.99.3 3163 HMOX2, HO-2 1.14.99.3
644 BLVRA, BLVR 1.3.1.24 645 BLVRB, FLR 1.3.1.24 2232 FDXR, ADXR
1.6.99.1 1.18.1.2 3052 HCCS, CCHL 4.4.1.17 1356 CP 1.16.3.1 9.11
Ubiquinone biosynthesis PATH:hsa00130 4938 OAS1, IFI-4, OIAS
2.7.7.-- 4939 OAS2, P69 2.7.7.-- 5557 PRIM1 2.7.7.-- 5558 PRIM2A,
PRIM2 2.7.7.-- 5559 PRIM2B, PRIM2 2.7.7.-- 7015 TERT, EST2, TCS1,
TP2, TRT 2.7.7.-- 8638 OASL, TRIP14 2.7.7.-- 10. Metabolism of
Other Substances 10.1 Terpenoid biosynthesis PATH:hsa00900 10.2
Flavonoids, stilbene and lignin biosynthesis PATH:hsa00940 10.3
Alkaloid biosynthesis I PATH:hsa00950 10.4 Alkaloid biosynthesis II
PATH:hsa00960 10.6 Streptomycin biosynthesis PATH:hsa00521 10.7
Erythromycin biosynthesis PATH:hsa00522 10.8 Tetracycline
biosynthesis PATH:hsa00253 10.14 gamma-Hexachlorocyclohexane
degradation PATH:hsa00361 5444 PON1, ESA, PON 3.1.8.1 3.1.1.2 5445
PON2 3.1.1.2 3.1.8.1 10.18 1,2-Dichloroethane degradation
PATH:hsa00631 10.20 Tetrachloroethene degradation PATH:hsa00625
2052 EPHX1, EPHX, MEH 3.3.2.3 2053 EPHX2 3.3.2.3 10.21 Styrene
degradation PATH:hsa00643 11. Transcription (condensed) 11.1 RNA
polymerase PATH:hsa03020 11.2 Transcription factors PATH:hsa03022
12. Translation (condensed) 12.1 Ribosome PATH:hsa03010 12.2
Translation factors PATH:hsa03012 1915 EEF1A1, EF1A, ALPHA, EEF-1,
EEF1A 3.6.1.48 1917 EEF1A2, EF1A 3.6.1.48 1938 EEF2, EF2, EEF-2
3.6.1.48 12.3 Aminoacyl-tRNA biosynthesis PATH:hsa00970 13. Sorting
and Degradation (condensed) 13.1 Protein export PATH:hsa03060 23478
SPC18 3.4.21.89 13.4 Proteasome PATH:hsa03050 5687 PSMA6, IOTA,
PROS27 3.4.99.46 5683 PSMA2, HC3, MU, PMSA2, PSC2 3.4.99.46 5685
PSMA4, HC9 3.4.99.46 5688 PSMA7, XAPC7 3.4.99.46 5686 PSMA5, ZETA,
PSC5 3.4.99.46 5682 PSMA1, HC2, NU, PROS30 3.4.99.46 5684 PSMA3,
HC8 3.4.99.46 5698 PSMB9, LMP2, RING12 3.4.99.46 5695 PSMB7, Z
3.4.99.46 5691 PSMB3, HC10-II 3.4.99.46 5690 PSMB2, HC7-I 3.4.99.46
5693 PSMB5, LMPX, MB1 3.4.99.46 5689 PSMB1, HCS, PMSB1 3.4.99.46
5692 PSMB4, HN3, PROS26 3.4.99.46 14. Replication and Repair 14.1
DNA polymerase PATH:hsa03030 14.2 Replication Complex PATH:hsa03032
23626 SPO11 5.99.1.3 7153 TOP2A, TOP2 5.99.1.3 7155 TOP2B 5.99.1.3
7156 TOP3A, TOP3 5.99.1.2 8940 TOP3B 5.99.1.2 22. Enzyme Complex
22.1 Electron Transport System, Complex I PATH:hsa03100 22.2
Electron Transport System, Complex II PATH:hsa03150 22.3 Electron
Transport System, Complex III PATH:hsa03140 22.4 Electron Transport
System, Complex IV PATH:hsa03130 22.5 ATP Synthase PATH:hsa03110
22.8 ATPases PATH:hsa03230 23. Unassigned 23.1 Enzymes 5538 PPT1,
CLN1, PPT, INCL C160ACP + H2O -> C160 + ACP 3.1.2.22 23.2
Non-enzymes 22934 RPIA, RPI RL5P <-> R5P 5.3.1.6 5250
SLC25A3, PHC PI + H <-> Hm + Plm 6576 CIT + MALm <->
CITm + MAL 51166 LOC51168 AADP + AKG -> GLU + KADP 2.6.1.39 5625
PRODH PRO + FAD -> P5C + FADH2 1.5.3.-- 6517 SLC2A4, GLUT4 GLCxt
-> GLC 6513 SLC2A1, GLUT1, GLUT GLCxt -> GIG 26275 HIBCH,
HIBYL-COA-H HIBCOAm + H2Om -> HIBm + COAm 3.1.2.4 23305
KIAA0837, ACS2, LACS5, IACS2 C160 + COA + ATP -> AMP + PPI +
C160COA 8611 PPAP2A, PAP-2A PA + H2O -> DAGLY + PI 8612 PPAP2C,
PAP-2C PA + H2O -> DAGLY + PI 8613 PPAP2B, PAP-28 PA + H2O ->
DAGLY + PI 56994 LOC56994 CDPCHO + DAGLY -> PC + CMP 10400 PEMT,
PEMT2 SAM + PE -> SAH + PMME 5833 PCYT2, ET PETHM + CTP ->
CDPETN + PPI 10390 CEPT1 CDPETN + DAGLY <-> CMP + PE 8394
PIP5K1A PINS4P + ATP -> D45PI + ADP 8395 P1P5K1B, STMT, MSS4
PINS4P + ATP -> D45PI + ADP 8396 PIP5K2B PINS4P + ATP ->
D45PI + ADP 23396 PIP5K1C, KIAA0589, PIP5K-GAMMA PINS4P + ATP ->
D45PI + ADP 24. Our own reactions which need to be found in KEGG
GL3P <-> GL3Pm T3P2 <-> T3P2m PYR <-> PYRm + Hm
ADP + ATPm + PI + H -> Hm + ADPm + ATP + Plm AKG + MALm
<-> AKGm + MAL ASPm + GLU + H -> Hm + GLUm + ASP GDP +
GTPm + PI + H -> Hm + GDPm + GTP + Plm C160Axt + FABP ->
C160FP + ALBxt C160FP -> C160 + FABP C180Axt + FABP -> C180FP
+ ALBxt C180FP -> C180 + FABP C161Axt + FABP -> C161FP +
ALBxt C161FP -> C161 + FABP C181Axt + FABP -> C181FP + ALBxt
C181FP -> C181 + FABP C182Ax1 + FABP -> C182FP + ALBxt C182FP
-> C182 + FABP C204Axt + FABP -> C204FP + ALBxt C204FP ->
C204 + FABP O2xt -> O2 O2 <-> O2m ACTACm + SUCCOAm ->
SUCCm + AACCOAm 3HB -> 3HBm MGCOAm + H2Om -> H3MCOAm 4.2.1.18
OMVAL -> OMVALm OIVAL -> OIVALm OICAP -> OICAPm Cl60CAR
<-> Cl60CARm CAR <-> CARm DMMCOAm -> LMMCOAm
5.1.99.1 amino acid metabolism THR -> NH3 + H2O + OBUT 4.2.1.16
THR + NAD -> CO2 + NADH + AMA 1.1.1.103 THR + NAD + COA ->
NADH + ACCOA + GLY AASA + NAD -> NADH + AADP 1.2.1.3.1 FKYN +
H2O -> FOR + KYN 3.5.1.9 CMUSA -> CO2 + AM6SA 4.1.1.45 AM6SA
+ NAD -> AMUCO + NADH 1.2.1.32 AMUCO + NADPH -> KADP + NADP +
NH4 1.5.1.-- CYSS + AKG <-> GLU + SPYR URO + H2O -> 4I5P
4.2.1.49 4I5P + H2O -> FIGLU 3.5.2.7 GLU <-> GLUm + Hm ORN
+ Hm -> ORNm ORN + Hm + CITRm <-> CITR + ORNm GLU + ATP +
NADPH -> NADP + ADP + PI + GLUGSAL GLYAm + ATPm -> ADPm +
2PGm AM6SA -> PIC SPYR + H2O -> H2S03 + PYR P5C <->
GLUGSAL fatty acid synthesis MALCOA + ACP <-> MALACP + COA
2.3.1.39 ACCOA + ACP <-> AC ACP + COA ACACP + 4 MALACP + 8
NADPH -> 8 NADP + C100ACP + 4 CO2 + 4 ACP ACACP + 5 MALACP + 10
NADPH -> 10 NADP + C120ACP + 5 CO2 + 5 ACP ACACP + 6 MALACP + 12
NADPH -> 12 NADP + C140ACP + 6 CO2 + 6 ACP ACACP + 6 MALACP + 11
NADPH -> 11 NADP + C141ACP + 6 CO2 + 6 ACP ACACP + 7 MALACP + 14
NADPH -> 14 NADP + C160ACP + 7 CO2 + 7 ACP ACACP + 7 MALACP + 13
NADPH -> 13 NADP + C181ACP + 7 CO2 + 7 ACP ACACP + 8 MALACP + 16
NADPH -> 16 NADP + C180ACP + 8 CO2 + 8 ACP ACACP + 8 MALACP + 15
NADPH -> 15 NADP + C181ACP + 8 CO2 + 8 ACP ACACP + 8 MALACP + 14
NADPH -> 14 NADP + C182ACP + 8 CO2 + 8 ACP C160COA + CAR ->
C160CAR + COA C160CARm + COAm -> C160COAm + CARm fatty acid
depredation GL3P + 0.017 C100ACP + 0.062 C120ACP + 0.1 C140ACP +
0.27 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP +
0.093 C182ACP -> AGL3P + ACP TAGLYm + 3 H2Om -> GLm + 3 C160m
Phospholipid metabolism SAM + PMME -> SAH + PDME PDME + SAM
-> PC + SAH PE + SER <-> PS + ETHM Muscle contraction
MYOACT + ATP -> MYOATP + ACTIN MYOATP + ACTIN -> MYOADPAC
MYOADPAC -> ADP + PI + MYOACT + CONTRACT
TABLE-US-00010 TABLE 2 // Homo Sapiens Core Metabolic Network // //
Glycolysis // -1 GLC -1 ATP +1 G6P +1 ADP 0 HK1 -1 G6P -1 H2O +1
GLC +1 PI 0 G6PC -1 G6P +1 F6P 0 GPIR -1 F6P -1 ATP +1 FDP +1 ADP 0
PFKL -1 FDP -1 H2O +1 F6P +1 PI 0 FBP1 -1 FDP +1 T3P2 +1 T3P1 0
ALDOAR -1 T3P2 +1 T3P1 0 TPI1R -1 T3P1 -1 PI -1 NAD +1 NADH +1
13PDG 0 GAPDR -1 13PDG -1 ADP +1 3PG +1 ATP 0 PGK1R -1 13PDG +1
23PDG 0 PGAM1 -1 23PDG -1 H2O +1 3PG +1 PI 0 PGAM2 -1 3PG +1 2PG 0
PGAM3R -1 2PG +1 PEP +1 H2O 0 ENO1R -1 PEP -1 ADP +1 PYR +1 ATP 0
PKLR -1 PYRm -1 COAm -1 NADm +1 NADHm +1 CO2m +1 ACCOAm 0 PDHA1 -1
NAD -1 LAC +1 PYR +1 NADH 0 LDHAR -1 GIP +1 G6P 0 PGM1R // TCA //
-1 ACCOAm -1 OAm -1 H2Om +1 COAm +1 CITm 0 CS -1 CIT +1 ICIT 0
ACO1R -1 CITm +1 ICITm 0 ACO2R -1 ICIT -1 NADP +1 NADPH +1 CO2 +1
AKG 0 IDH1 -1 ICITm -1 NADPm +1 NADPHm +1 CO2m +1 AKGm 0 IDH2 -1
ICITm -1 NADm +1 CO2m +1 NADHm +1 AKGm 0 IDH3A -1 AKGm -1 NADm -1
COAm +1 CO2m +1 NADHm +1 SUCCOAm 0 OGDH -1 GTPm -1 SUCCm -1 COAm +1
GDPm +1 Pim +1 SUCCOAm 0 SUCLG1R -1 ATPm -1 SUCCm -1 COAm +1 ADPm
+1 Pim +1 SUCCOAm 0 SUCLA2R -1 FUMm -1 H2Om +1 MALm 0 FHR -1 MAL -1
NAD +1 NADH +1 OA 0 MDH1R -1 MALm -1 NADm +1 NADHm +1 OAm 0 MDH2R
-1 PYRm -1 ATPm -1 CO2m +1 ADPm +1 OAm +1 Pim 0 PC -1 OA -1 GTP +1
PEP +1 GDP +1 CO2 0 PCK1 -1 OAm -1 GTPm +1 PEPm +1 GDPm +1 CO2m 0
PCK2 -1 ATP -1 CIT -1 COA -1 H2O +1 ADP +1 PI +1 ACCOA +1 OA 0 ACLY
// PPP // -1 G6P -1 NADP +1 D6PGL +1 NADPH 0 G6PDR -1 D6PGL -1 H2O
+1 D6PGC 0 PGLS -1 D6PGC -1 NADP +1 NADPH +1 CO2 +1 RL5P 0 PGD -1
RL5P +1 X5P 0 RPER -1 R5P -1 X5P +1 T3P1 +1 S7P 0 TKT1R -1 X5P -1
E4P +1 F6P +1 T3P1 0 TKT2R -1 T3P1 -1 S7P +1 E4P +1 F6P 0 TALD01R
-1 RL5P +1 R5P 0 RPIAR // Glycogen // -1 G1P -1 UTP +1 UDPG +1 PPI
0 UGP1 -1 UDPG +1 UDP +1 GLYCOGEN 0 GYSl -1 GLYCOGEN -1 PI +1 GlP 0
GBE1 // ETS // -1 MALm -1 NADPm +1 CO2m +1 NADPHm +1 PYRm 0 ME3 -1
MALm -1 NADm +1 CO2m +1 NADHm +1 PYRm 0 ME2 -1 MAL -1 NADP +1 CO2
+1 NADPH +1 PYR 0 ME1 -1 NADHm -1 Qm -4 Hm +1 QH2m +1 NADm +4 H 0
MTND1 -1 SUCCm -1 FADm +1 FUMm +1 FADH2m 0 SDHC1R -1 FADH2m -1 Qm
+1 FADm +1 QH2m 0 SDHC2R -1 O2m -4 FEROm -4 Hm +4 FERIm +2 H2Om +4
H 0 UQCRFS1 -1 QH2m -2 FERIm -4 Hm +1 Qm +2 FEROm +4 H 0 COX5BL4 -1
ADPm -1 PIm -3 H +1 ATPm +3 Hm +1 H2Om 0 MTAT -1 ADP -1 ATPm -1 PI
-1 H +1 Hm +1 ADPm +1 ATP +1 PIm 0 ATPMC -1 GDP -1 GTPm -1 PI -1 H
+1 Hm +1 GDPm +1 GTP +1 PIm 0 GTPMC -1 PPI +2 PI 0 PP -1 ACCOA -1
ATP -1 CO2 +1 MALCOA +1 ADP +1 PI 0 ACACAR -1 GDP -1 ATP +1 GTP +1
ADP 0 GOT3R // Transporters // -1 CIT -1 MALm +1 CITm +1 MAL 0
CITMCR -1 PYR -1 H +1 PYRm +1 Hm 0 PYRMCR // Glycerol Phosphate
Shuttle // -1 GL3Pm -1 FADm +1 T3P2m +1 FADH2m 0 GPD2 -1 T3P2 -1
NADH +1 GL3P +1 NAD 0 GPD1 -1 GL3P +1 GL3Pm 0 GL3PMCR -1 T3P2 +1
T3P2m 0 T3P2MCR // Malate/Aspartate Shuttle // -1 OAm -1 GLUm +1
ASPm +1 AKGm 0 GOT1R -1 ASP -1 AKG +1 OA +1 GLU 0 GOT2R -1 AKG -1
MALm +1 AKGm +1 MAL 0 MALMCR -1 ASPm -1 GLU -1 H +1 Hm +1 GLUm +1
ASP 0 ASPMC // Exchange Fluxes // +1 GLC 0 GLCexR +1 PYR 0 PYRexR
+1 CO2 0 CO2exR +1 O2 0 O2exR +1 PI 0 PIexR +1 H2O 0 H2OexR +1 LAC
0 LACexR +1 CO2m 0 CO2min -1 CO2m 0 CO2mout +1 O2m 0 02min -1 O2m 0
02mout +1 H2Om 0 H2Omin -1 H2Om 0 H2Omout +1 PIm 0 PImin -1 PIm 0
Pimout // Output // -1 ATP +1 ADP +1 PI 0 Output 0.0 end end E 0
max 1 Output 0 end 0 GLCexR 1 -1000 PYRexR0 -1000 LACexR 0 0 end 0
rev. rxn 33 nonrev. rxn 31 total rxn 64 matrix columns 97 unique
enzymes 52
TABLE-US-00011 TABLE 3 Abbrev. Reaction Rxn Name Glycolysis HK1 GLC
+ ATP -> G6P + ADP HK1 G6PC, G6PT G6P + H2O -> GLC + PI G6PC
GPI G6P <-> F6P GPI PFKL F6P + ATP -> FDP + ADP PFKL FBP1,
FBP FDP + H2O -> F6P + PI FBP1 ALDOA FDP <-> T3P2 + T3P1
ALDOA TPI1 T3P2 <-> T3P1 TPI1 GAPD, GAPDH T3P1 + PI + NAD
<-> NADH + 13PDG GAPD PGK1, PGKA 13PDG + ADP <-> 3PG +
ATP PGK1 PGAM1, PGAMA 13PDG <-> 23PDG PGAM1 23PDG + H2O ->
3PG + PI PGAM2 3PG <-> 2PG PGAM3 EN01, PPH, EN01L1 2PG
<-> PEP + H2O EN01 PKLR, PK1 PEP + ADP -> PYR + ATP PKLR
PDHA1, PHE1A, PDHA PYRm COAm + NADm -> + NADHm + CO2m + ACCOAm
PDHA1 LDHA, LDH1 NAD + LAC <-> PYR + NADH LDHA PGM1 G1P
<-> G6P PGM1 TCA CS ACCOAm + OAm + H2Om -> COAm + CITm CS
ACO1, IREB1, IRP1 CIT <-> ICIT ACO1 ACO2 CITm <-> ICITm
ACO2 IDH1 ICIT + NADP -> NADPH + CO2 + AKG IDH1 IDH2 ICITm +
NADPm -> NADPHm + CO2m + AKGm IDH2 IDH3A ICITm + NADm -> CO2m
+ NADHm + AKGm IDH3A OGDH AKGm + NADm + COAm -> CO2m + NADHm +
SUCCOAm OGDH SUCLG1, SUCLA1 GTPm + SUCCm + COAm <-> GDPm +
PIm + SUCCOAm SUCLG1 SUCLA2 ATPm + SUCCm + COAm <-> ADPm +
Pim + SUCCOAm SUCLA2 FH FUMm + H2Om <-> MALm FH MDH1 MAL +
NAD <-> NADH + OA MDH1 MDH2 MALm + NADm <-> NADHm + OAm
MDH2 PC, PCB PYRm + ATPm + CO2m -> ADPm + OAm + PIm PC ACLY,
ATPCL, CLATP ATP + CIT + COA + H2O -> ADP + PI + ACCOA + OA ACLY
PCK1 OA + GTP -> PEP + GDP + CO2 PCK1 PPP G6PD, G6PD1 G6P + NADP
<-> D6PGL + NADPH G6PD PGLS, 6PGL D6PGL + H2O -> D6PGC
PGLS PGD D6PGC + NADP -> NADPH + CO2 + RL5P PGD RPE RL5P
<-> X5P RPE TKT R5P + X5P <-> T3P1 + S7P TKT1 X5P + E4P
<-> F6P + T3P1 TKT2 TALDO1 T3P1 + S7P <-> E4P + F6P
TALDO1 UGP1 G1P + UTP -> UDPG + PPI UGP1 ACACA, ACAC, ACC ACCOA
+ ATP + CO2 <-> MALCOA + ADP + PI + H ACACA ETS ME3 MALm +
NADPm -> CO2m + NADPHm + PYRm ME3 MTND1 NADHm + Qm + 4 Hm ->
QH2m + NADm + 4 H MTND1 SDHC SUCCm + FADm <-> FUMm + FADH2m
SDHC1 FADH2m + Qm <-> FADm + QH2m SDHC2 UQCRFS1, RIS1 O2m + 4
FEROm + 4 Hm -> 4 FERIm + 2 H2Om + 4 H UQCRFS1 COX5BL4 QH2m + 2
FERIm + 4 Hm -> Qm + 2 FEROm + 4 H COX5BL4 MTATP6 ADPm + PIm + 3
H -> ATPm + 3 Hm + H2Om MTAT PP, SID6-8061 PPI -> 2 PI PP
Malate Aspartate shunttle GOT1 OAm + GLUm <-> ASPm + AKGm
GOT1 GOT2 OA + GLU <-> ASP + AKG GOT2 GDP + ATP <-> GTP
+ ADP GOT3 Glycogen GBE1 GLYCOGEN + PI -> G1P GBE1 GYS1, GYS
UDPG -> UDP + GLYCOGEN GYS1 Glycerol Phosphate Shunttle GPD2
GL3Pm + FADm -> T3P2m + FADH2m GPD2 GPD1 T3P2 + NADH -> GL3P
+ NAD GPD1 RPIA, RPI RL5P <-> R5P RPIA Mitochondria Transport
CIT + MALm <-> CITm + MAL CITMC GL3P <-> GL3Pm GL3PMC
T3P2 <-> T3P2m T3P2MC PYR <-> PYRm + Hm PYRMC ADP +
ATPm + PI + H -> Hm + ADPm + ATP + PIm ATPMC AKG + MALm
<-> AKGm + MAL MALMC ASPm + GLU + H -> Hm + GLUm + ASP
ASPMC GDP + GTPm + PI + H -> Hm + GDPm + GTP + PIm GTPMC
TABLE-US-00012 TABLE 4 Metabolic Reaction for Muscle Cells Reaction
Rxt Name GLC + ATP -> G6P + ADP 0 HK1 G6P <-> F6P 0 GPI
F6P + ATP -> FDP + ADP 0 PFKL1 FDP + H2O -> F6P + PI 0 FBP1
FDP <-> T3P2 + T3P1 0 ALDOA T3P2 <-> T3P1 0 TPI1 T3P1 +
PI + NAD <-> NADH + 13PDG 0 GAPD 13PDG + ADP <-> 3PG +
ATP 0 PGK1 3PG <-> 2PG 0 PGAM3 2PG <-> PEP + H2O 0 ENO1
PEP + ADP -> PYR + ATP 0 PK1 PYRm + COAm + NADm -> + NADHm +
CO2m + ACCOAm 0 PDHA1 NAD + LAC <-> PYR + NADH 0 LDHA G1P
<-> G6P 0 PGM1 ACCOAm + OAm + H2Om -> COAm + CITm 0 CS CIT
<-> ICIT 0 ACO1 CITm <-> ICITm 0 ACO2 ICIT + NADP ->
NADPH + CO2 + AKG 0 IDH1 ICITm + NADPm -> NADPHm + CO2m + AKGm 0
IDH2 ICITm + NADm -> CO2m + NADHm + AKGm 0 IDH3A AKGm + NADm +
COAm -> CO2m + NADHm + SUCCOAm 0 OGDH GTPm + SUCCm + COAm
<-> GDPm + PIm + SUCCOAm 0 SUCLG1 ATPm + SUCCm + COAm
<-> ADPm + PIm + SUCCOAm 0 SUCLA2 FUMm + H2Om <-> MALm
0 FH MAL + NAD <-> NADH + OA 0 MDH1 MALm + NADm <->
NADHm + OAm 0 MDH2 PYRm + ATPm + CO2m -> ADPm + OAm + PIm 0 PC
ATP + CIT + COA + H2O -> ADP + PI + ACCOA + OA 0 ACLY OA + GTP
-> PEP + GDP + CO2 0 PCK1 OAm + GTPm -> PEPm + GDPm + CO2m 0
PCK2 G6P + NADP <-> D6PGL + NADPH 0 G6PD D6PGL + H2O ->
D6PGC 0 H6PD D6PGC + NADP -> NADPH + CO2 + RL5P 0 PGD RL5P
<-> X5P 0 RPE R5P + X5P <-> T3P1 + S7P 0 TKT1 X5P + E4P
<-> F6P + T3P1 0 TKT2 T3P1 + S7P <-> E4P + F6P 0 TALDO1
RL5P <-> R5P 0 RPIA G1P + UTP -> UDPG + PPI 0 UGP1
GLYCOGEN + PI -> G1P 0 GBE1 UDPG -> UDP + GLYCOGEN 0 GYS1
MALm + NADm -> CO2m + NADHm + PYRm 0 ME2 MALm + NADPm -> CO2m
+ NADPHm + PYRm 0 ME3 MAL + NADP -> CO2 + NADPH + PYR 0 HUMNDME
NADHm + Qm + 4 Hm -> QH2m + NADm + 4 H 0 MTND1 SUCCm + FADm
<-> FUMm + FADH2m 0 SDHC1 FADH2m + Qm <-> FADm + QH2m 0
SDHC2 O2m + 4 FEROm + 4 Hm -> 4 FERIm + 2 H2Om + 4 H 0 UQCRFS1
QH2m + 2 FERIm + 4 Hm -> Qm + 2 FEROm + 4 H 0 COX5BL4 ADPm + PIm
+ 3 H -> ATPm + 3 Hm + H2Om 0 MTAT1 ADP + ATPm + PI + H -> Hm
+ ADPm + ATP + PIm 0 ATPMC GDP + GTPm + PI + H -> Hm + GDPm +
GTP + PIm 0 GTPMC PPI -> 2 PI 0 PP GDP + ATP <-> GTP + ADP
0 NME1 ACCOA + ATP + CO2 <-> MALCOA + ADP + PI + H 0 ACACA
MALCOA + ACP <-> MALACP + COA 0 FAS1_1 ACCOA + ACP <->
ACACP + COA 0 FAS1_2 ACACP + 4 MALACP + 8 NADPH -> 8 NADP +
C100ACP + 4 CO2 + 4 ACP 0 C100SY ACACP + 5 MALACP + 10 NADPH ->
10 NADP + C120ACP + 5 CO2 + 5 0 C120SY ACP ACACP + 6 MALACP + 12
NADPH -> 12 NADP + C140ACP + 6 CO2 + 6 0 C140SY ACP ACACP + 6
MALACP + 11 NADPH -> 11 NADP + C141ACP + 6 CO2 + 6 0 C141SY ACP
ACACP + 7 MALACP + 14 NADPH -> 14 NADP + C160ACP + 7 CO2 + 7 0
C160SY ACP ACACP + 7 MALACP + 13 NADPH -> 13 NADP + C161ACP + 7
CO2 + 7 0 C161SY ACP ACACP + 8 MALACP + 16 NADPH -> 16 NADP +
C180ACP + 8 CO2 + 8 0 C180SY ACP ACACP + 8 MALACP + 15 NADPH ->
15 NADP + C181ACP + 8 CO2 + 8 0 C181SY ACP ACACP + 8 MALACP + 14
NADPH -> 14 NADP + C182ACP + 8 CO2 + 8 0 C182SY ACP C160ACP +
H2O -> C160 + ACP 0 PPT1 C160 + COA + ATP -> AMP + PPI +
C160COA 0 KIAA C160COA + CAR -> C160CAR + COA 0 C160CA C160CARm
+ COAm -> C160COAm + CARm 0 C160CB C160CARm + COAm + FADm + NADm
-> FADH2m + NADHm + 0 HADHA C140COAm + ACCOAm C140COAm + 7 COAm
+ 7 FADm + 7 NADm -> 7 FADH2m + 7 NADHm + 7 0 HADH2 ACCOAm
TAGLYm + 3 H2Om -> GLm + 3 C160m 0 TAGRXN GL3P + 0.017 C100ACP +
0.062 C120ACP + 0.1 C140ACP + 0.27 0 GAT1 C160ACP + 0.169 C161ACP +
0.055 C180ACP + 0.235 C181ACP + 0.093 C182ACP -> AGL3P + ACP
AGL3P + 0.017 C100ACP + 0.062 C120ACP + 0.100 C140ACP + 0.270 0
AGPAT1 C160ACP + 0.169 C161ACP + 0.055 C180ACP + 0.235 C181ACP +
0.093 C182ACP -> PA + ACP ATP + CHO -> ADP + PCHO 0 CHKLT1
PCHO + CTP -> CDPCHO + PPI 0 PCYT1A CDPCHO + DAGLY -> PC +
CMP 0 LOC SAM + PE -> SAH + PMME 0 PEMT SAM + PMME -> SAH +
PDME 0 MFPS PDME + SAM -> PC + SAH 0 PNMNM G6P -> MI1P 0
ISYNA1 MI1P -> MYOI + PI 0 IMPA1 PA + CTP <-> CDPDG + PPI
0 CDS1 CDPDG + MYOI -> CMP + PINS 0 PIS ATP + PINS -> ADP +
PINSP 0 PIK3CA ATP + PINS -> ADP + PINS4P 0 PIK4CA PINS4P + ATP
-> D45PI + ADP 0 PIP5K1 D45PI -> TPI + DAGLY 0 PLCB2 PA + H2O
-> DAGLY + PI 0 PPAP2A DAGLY + 0.017 C100ACP + 0.062 C120ACP +
0.100 C140ACP + 0.270 0 DGAT C160ACP + 0.169 C161ACP + 0.055
C180ACP + 0.235 C181ACP + 0.093 C182ACP -> TAGLY + ACP CDPDG +
SER <-> CMP + PS 0 PTDS CDPETN + DAGLY <-> CMP + PE 0
CEPT1 PE + SER <-> PS + ETHM 0 PESER ATP + ETHM -> ADP +
PETHM 0 EKI1 PETHM + CTP -> CDPETN + PPI 0 PCYT2 PS -> PE +
CO2 0 PISD 3HBm + NADm -> NADHm + Hm + ACTACm 0 BDH ACTACm +
SUCCOAm -> SUCCm + AACOAm 0 3OCT THF + SER <-> GLY +
METTHF 0 SHMT1 THFm + SERm <-> GLYm + METTHFm 0 SHMT2 SERm +
PYRm <-> ALAm + 3HPm 0 AGXT 3PG + NAD <-> NADH + PHP 0
PHGDH PHP + GLU <-> AKG + 3PSER 0 PSA 3PSER + H2O -> PI +
SER 0 PSPH 3HPm + NADHm -> NADm + GLYAm 0 GLYD SER -> PYR +
NH3 + H2O 0 SDS GLYAm + ATPm -> ADPm + 2PGm 0 GLTK PYR + GLU
<-> AKG + ALA 0 GPT GLUm + CO2m + 2 ATPm -> 2 ADPm + 2 PIm
+ CAPm 0 CPS1 AKGm + NADHm + NH3m <-> NADm + H2Om + GLUm 0
GLUD1 AKGm + NADPHm + NH3m <-> NADPm + H2Om + GLUm 0 GLUD2
GLUm + NH3m + ATPm -> GLNm + ADPm + PIm 0 GLUL ASPm + ATPm +
GLNm -> GLUm + ASNm + AMPm + PPIm 0 ASNS ORN + AKG <->
GLUGSAL + GLU 0 OAT GLU <-> GLUm + Hm 0 GLUMT GLU + ATP +
NADPH -> NADP + ADP + PI + GLUGSAL 0 P5CS GLUP + NADH -> NAD
+ PI + GLUGSAL 0 PYCS P5C <-> GLUGSAL 0 SPTC HIS -> NH3 +
URO 0 HAL URO + H2O -> 4I5P 0 UROH 4I5P + H2O -> FIGLU 0 IMPR
FIGLU + THF -> NFTHF + GLU 0 FTCD MET + ATP + H2O -> PPI + PI
+ SAM 0 MAT1A SAM + DNA -> SAH + DNA5MC 0 DNMT1 SAH + H2O ->
HCYS + ADN 0 AHCYL1 HCYS + MTHF -> THF + MET 0 MTR SER + HCYS
-> LLCT + H2O 0 CBS LLCT + H2O -> CYS + HSER 0 CTH1 OBUT +
NH3 <-> HSER 0 CTH2 CYS + O2 <-> CYSS 0 CDO1 CYSS + AKG
<-> GLU + SPYR 0 CYSAT SPYR + H2O -> H2SO3 + PYR 0 SPTB
LYS + NADPH + AKG -> NADP + H2O + SAC 0 LKR1 SAC + H2O + NAD
-> GLU + NADH + AASA 0 LKR2 AASA + NAD -> NADH + AADP 0 2ASD
AADP + AKG -> GLU + KADP 0 LOC5 TRP + O2 -> FKYN 0 TDO2 FKYN
+ H2O -> FOR + KYN 0 KYNF KYN + NADPH + O2 -> HKYN + NADP +
H2O 0 KMO HKYN + H2O -> HAN + ALA 0 KYNU2 HAN + O2 -> CMUSA 0
HAAO CMUSA -> CO2 + AM6SA 0 ACSD AM6SA -> PIC 0 SPTA AM6SA +
NAD -> AMUCO + NADH 0 AMSD AMUCO + NADPH -> KADP + NADP + NH4
0 2AMR ARG -> ORN + UREA 0 ARG2 ORN + Hm -> ORNm 0 ORNMT ORN
+ Hm + CITRm <-> CITR + ORNm 0 ORNCITT ORNm + CAPm ->
CITRm + PIm + Hm 0 OTC CITR + ASP + ATP <-> AMP + PPI +
ARGSUCC 0 ASS ARGSUCC -> FUM + ARG 0 ASL PRO + FAD -> P5C +
FADH2 0 PRODH P5C + NADPH -> PRO + NADP 0 PYCR1 THR -> NH3 +
H2O + OBUT 0 WTDH THR + NAD -> CO2 + NADH + AMA 0 TDH AMA + H2O
+ FAD -> NH3 + FADH2 + MTHGXL 0 MAOA GLYm + THFm + NADm
<-> METTHFm + NADHm + CO2m + NH3m 0 AMT PHE + THBP + O2 ->
TYR + DHBP + H2O 0 PAH NADPH + DHBP -> NADP + THBP 0 QDPR AKG +
TYR -> HPHPYR + GLU 0 TAT HPHPYR + O2 -> HGTS + CO2 0 HPD
HGTS + O2 -> MACA 0 HGD MACA -> FACA 0 GSTZ1 FACA + H2O ->
FUM + ACA 0 FAH AKG + ILE -> OMVAL + GLU 0 BCAT1A OMVALm + COAm
+ NADm -> MBCOAm + NADHm + CO2m 0 BCKDHAA MBCOAm + FADm ->
MCCOAm + FADH2m 0 ACADMA MCCOAm + H2Om -> MHVCOAm 0 ECHS1B
MHVCOAm + NADm -> MAACOAm + NADHm 0 EHHADHA MAACOAm -> ACCOAm
+ PROPCOAm 0 ACAA2 2 ACCOAm <-> COAm + AACCOAm 0 ACATm1 AKG +
VAL -> OIVAL + GLU 0 BCAT1B OIVALm + COAm + NADm -> IBCOAm +
NADHm + CO2m 0 BCKDHAB IBCOAm + FADm -> MACOAm + FADH2m 0 ACADSB
MACOAm + H2Om -> HIBCOAm 0 EHHADHC HIBCOAm + H2Om -> HIBm +
COAm 0 HIBCHA HIBm + NADm -> MMAm + NADHm 0 EHHADHB MMAm + COAm
+ NADm -> NADHm + CO2m + PROPCOAm 0 MMSDH PROPCOAm + CO2m + ATPm
-> ADPm + PIm + DMMCOAm 0 PCCA DMMCOAm -> LMMCOAm 0 HIBCHF
LMMCOAm -> SUCCOAm 0 MUT AKG + LEU -> OICAP + GLU 0 BCAT1C
OICAPm + COAm + NADm -> IVCOAm + NADHm + CO2m 0 BCKDHAC OICAPm +
COAm + NADH -> IVCOAm + NADHm + CO2m 0 BCKDHBC OICAPm + COAm +
NADHm -> IVCOAm + NADHm + CO2m 0 DBTC IVCOAm + FADm ->
MCRCOAm + FADH2m 0 IVD MCRCOAm + ATPm + CO2m + H2Om -> MGCOAm +
ADPm + PIm 0 MCCC1 MGCOAm + H2Om -> H3MCOAm 0 HIBCHB H3MCOAm
-> ACCOAm + ACTACm 0 HMGCL MYOACT + ATP -> MYOATP + ACTIN 0
MYOSA MYOATP + ACTIN -> MYOADPAC 0 MYOSB MYOADPAC -> ADP + PI
+ MYOACT + CONTRACT 0 MYOSC PCRE + ADP -> CRE + ATP 0 CREATA AMP
+ H2O -> PI + ADN 0 CREATB ATP + AMP <-> 2 ADP 0 CREATC O2
<-> O2m 0 O2MT 3HB -> 3HBm 0 HBMT CIT + MALm <->
CITm + MAL 0 CITMC PYR <-> PYRm + Hm 0 PYRMC C160CAR + COAm
-> C160COAm + CAR 0 C160CM OMVAL -> OMVALm 0 HIBCHC OIVAL
-> OIVALm 0 HIBCHD OICAP -> OICAPm 0 HIBCHE GL <-> GLm
0 GLMT GL3Pm + FADm -> T3P2m + FADH2m 0 GPD2 T3P2 + NADH
<-> GL3P + NAD 0 GPD1 GL3P <-> GL3Pm 0 GL3PMC T3P2
<-> T3P2m 0 T3P2MC OAm + GLUm <-> ASPm + AKGm 0 GOT1 OA
+ GLU <-> ASP + AKG 0 GOT2 AKG + MALm <-> AKGm + MAL 0
MALMC ASPm + GLU + H -> Hm + GLUm + ASP 0 ASPMC GLCxt -> GLC
0 GLUT4 O2xt -> O2 0 O2UP C160Axt + FABP -> C160FP + ALBxt 0
FAT1 C160FP -> C160 + FABP 0 FAT2 C180Axt + FABP -> C180FP +
ALBxt 0 FAT3 C180FP -> C180 + FABP 0 FAT4 C161Axt + FABP ->
C161FP + ALBxt 0 FAT5 C161FP -> C161 + FABP 0 FAT6 C181Axt +
FABP -> C181FP + ALBxt 0 FAT7 C181FP -> C181 + FABP 0 FAT8
C182Axt + FABP -> C182FP + ALBxt 0 FAT9 C182FP -> C182 + FABP
0 FAT10 C204Axt + FABP -> C204FP + ALBxt 0 FAT11 C204FP ->
C204 + FABP 0 FAT12 PYRxt + HEXT <-> PYR + H 0 PYRUP
LACxt + HEXT <-> LAC + HEXT 0 LACUP H <-> HEXT 0 HextUP
CO2 <-> CO2m 0 CO2MT H2O <-> H2Om 0 H2OMT ATP + AC +
COA -> AMP + PPI + ACCOA 0 FLJ2 C160CAR <-> C160CARm 0
C160MT CARm <-> CAR 0 CARMT CO2xt <-> CO2 0 CO2UP H2Oxt
<-> H2O 0 H2OUP PIxt + HEXT <-> HEXT + PI 0 PIUP
<-> GLCxt 0 GLCexR <-> PYRxt 0 PYRexR <-> CO2xt 0
CO2exR <-> O2xt 0 O2exR <-> PIxt 0 PlexR <->
H2Oxt 0 H2OexR <-> LACxt 0 LACexR <-> C160Axt 0
C160AexR <-> C161Axt 0 C161AexR <-> C180Axt 0 C180AexR
<-> C181Axt 0 C181AexR <-> C182Axt 0 C182AexR <->
C204Axt 0 C204AexR <-> ALBxt 0 ALBexR <-> 3HB 0 HBexR
<-> GLYCOGEN 0 GLYex <-> PCRE 0 PCREex <-> TAGLYm
0 TAGmex <-> ILE 0 ILEex <-> VAL 0 VALex <-> CRE
0 CREex <-> ADN 0 ADNex <-> PI 0 PIex
TABLE-US-00013 TABLE 5 Human Cell Types Keratinizing epithelial
cells Epidermal keratinocyte (differentiating epidermal cell)
Epidermal basal cell (stem cell) Keratinocyte of fingernails and
toenails Nail bed basal cell (stem cell) Medullary hair shaft cell
Cortical hair shaft cell Cuticular hair shaft cell Cuticular hair
root sheath cell Hair root sheath cell of Huxley's layer Hair root
sheath cell of Henle's layer External hair root sheath cell Hair
matrix cell (stem cell) Wet stratified barrier epithelial cells
Surface epithelial cell of stratified squamous epithelium of
cornea, tongue, oral cavity, esophagus, anal canal, distal urethra
and vagina basal cell (stem cell) of epithelia of cornea, tongue,
oral cavity, esophagus, anal canal, distal urethra and vagina
Urinary epithelium cell (lining urinary bladder and urinary ducts)
Exocrine secretory epithelial cells Salivary gland mucous cell
(polysaccharide-rich secretion) Salivary gland serous cell
(glycoprotein enzyme-rich secretion) Von Ebner's gland cell in
tongue (washes taste buds) Mammary gland cell (milk secretion)
Lacrimal gland cell (tear secretion) Ceruminous gland cell in ear
(wax secretion) Eccrine sweat gland dark cell (glycoprotein
secretion) Eccrine sweat gland clear cell (small molecule
secretion) Apocrine sweat gland cell (odoriferous secretion,
sex-hormone sensitive) Gland of Moll cell in eyelid (specialized
sweat gland) Sebaceous gland cell (lipid-rich sebum secretion)
Bowman's gland cell in nose (washes olfactory epithelium) Brunner's
gland cell in duodenum (enzymes and alkaline mucus) Seminal vesicle
cell (secretes seminal fluid components, including fructose for
swimming sperm) Prostate gland cell (secretes seminal fluid
components) Bulbourethral gland cell (mucus secretion) Bartholin's
gland cell (vaginal lubricant secretion) Gland of Littre cell
(mucus secretion) Uterus endometrium cell (carbohydrate secretion)
Isolated goblet cell of respiratory and digestive tracts (mucus
secretion) Stomach lining mucous cell (mucus secretion) Gastric
gland zymogenic cell (pepsinogen secretion) Gastric gland oxyntic
cell (hydrogen chloride secretion) Pancreatic acinar cell
(bicarbonate and digestive enzyme secretion) Paneth cell of small
intestine (lysozyme secretion) Type II pneumocyte of lung
(surfactant secretion) Clara cell of lung Hormone secreting cells
Anterior pituitary cells Somatotropes Lactotropes Thyrotropes
Gonadotropes Corticotropes Intermediate pituitary cell, secreting
melanocyte-stimulating hormone Magnocellular neurosecretory cells
secreting oxytocin secreting vasopressin Gut and respiratory tract
cells secreting serotonin secreting endorphin secreting
somatostatin secreting gastrin secreting secretin secreting
cholecystokinin secreting insulin secreting glucagon secreting
bombesin Thyroid gland cells thyroid epithelial cell parafollicular
cell Parathyroid gland cells Parathyroid chief cell oxyphil cell
Adrenal gland cells chromaffin cells secreting steroid hormones
(mineralcorticoids and gluco corticoids) Leydig cell of testes
secreting testosterone Theca interna cell of ovarian follicle
secreting estrogen Corpus luteum cell of ruptured ovarian follicle
secreting progesterone Kidney juxtaglomerular apparatus cell (renin
secretion) Macula densa cell of kidney Peripolar cell of kidney
Mesangial cell of kidney Epithelial absorptive cells (Gut, Exocrine
Glands and Urogenital Tract) Intestinal brush border cell (with
microvilli) Exocrine gland striated duct cell Gall bladder
epithelial cell Kidney proximal tubule brush border cell Kidney
distal tubule cell Ductulus efferens nonciliated cell Epididymal
principal cell Epididymal basal cell Metabolism and storage cells
Hepatocyte (liver cell) White fat cell Brown fat cell Liver
lipocyte Barrier function cells (Lung, Gut, Exocrine Glands and
Urogenital Tract) Type I pneumocyte (lining air space of lung)
Pancreatic duct cell (centroacinar cell) Nonstriated duct cell (of
sweat gland, salivary gland, mammary gland, etc.) Kidney glomerulus
parietal cell Kidney glomerulus podocyte Loop of Henle thin segment
cell (in kidney) Kidney collecting duct cell Duct cell (of seminal
vesicle, prostate gland, etc.) Epithelial cells lining closed
internal body cavities Blood vessel and lymphatic vascular
endothelial fenestrated cell Blood vessel and lymphatic vascular
endothelial continuous cell Blood vessel and lymphatic vascular
endothelial splenic cell Synovial cell (lining joint cavities,
hyaluronic acid secretion) Serosal cell (lining peritoneal,
pleural, and pericardial cavities) Squamous cell (lining
perilymphatic space of ear) Squamous cell (lining endolymphatic
space of ear) Columnar cell of endolymphatic sac with microvilli
(lining endolymphatic space of ear) Columnar cell of endolymphatic
sac without microvilli (lining endolymphatic space of ear) Dark
cell (lining endolymphatic space of ear) Vestibular membrane cell
(lining endolymphatic space of ear) Stria vascularis basal cell
(lining endolymphatic space of ear) Stria vascularis marginal cell
(lining endolymphatic space of ear) Cell of Claudius (lining
endolymphatic space of ear) Cell of Boettcher (lining endolymphatic
space of ear) Choroid plexus cell (cerebrospinal fluid secretion)
Pia-arachnoid squamous cell Pigmented ciliary epithelium cell of
eye Nonpigmented ciliary epithelium cell of eye Corneal endothelial
cell Ciliated cells with propulsive function Respiratory tract
ciliated cell Oviduct ciliated cell (in female) Uterine endometrial
ciliated cell (in female) Rete testis cilated cell (in male)
Ductulus efferens ciliated cell (in male) Ciliated ependymal cell
of central nervous system (lining brain cavities) Extracellular
matrix secretion cells Ameloblast epithelial cell (tooth enamel
secretion) Planum semilunatum epithelial cell of vestibular
apparatus of ear (proteoglycan secretion) Organ of Corti
interdental epithelial cell (secreting tectorial membrane covering
hair cells) Loose connective tissue fibroblasts Corneal fibroblasts
Tendon fibroblasts Bone marrow reticular tissue fibroblasts Other
nonepithelial fibroblasts Blood capillary pericyte Nucleus pulposus
cell of intervertebral disc Cementoblast/cementocyte (tooth root
bonelike cementum secretion) Odontoblast/odontocyte (tooth dentin
secretion) Hyaline cartilage chondrocyte Fibrocartilage chondrocyte
Elastic cartilage chondrocyte Osteoblast/osteocyte Osteoprogenitor
cell (stem cell of osteoblasts) Hyalocyte of vitreous body of eye
Stellate cell of perilymphatic space of ear Contractile cells Red
skeletal muscle cell (slow) White skeletal muscle cell (fast)
Intermediate skeletal muscle cell nuclear bag cell of Muscle
spindle nuclear chain cell of Muscle spindle Satellite cell (stem
cell) Ordinary heart muscle cell Nodal heart muscle cell Purkinje
fiber cell Smooth muscle cell (various types) Myoepithelial cell of
iris Myoepithelial cell of exocrine glands Red Blood Cell Blood and
Immune system cells Erythrocyte (red blood cell) Megakaryocyte
(platelet precursor) Monocyte Connective tissue macrophage (various
types) Epidermal Langerhans cell Osteoclast (in bone) Dendritic
cell (in lymphoid tissues) Microglial cell (in central nervous
system) Neutrophil granulocyte Eosinophil granulocyte Basophil
granulocyte Mast cell Helper T cell Suppressor T cell Cytotoxic T
cell B cells Natural killer cell Reticulocyte Stem cells and
committed progenitors for the blood and immune system (various
types) Sensory transducer cells Photoreceptor rod cell of eye
Photoreceptor blue-sensitive cone cell of eye Photoreceptor
green-sensitive cone cell of eye Photoreceptor red-sensitive cone
cell of eye Auditory inner hair cell of organ of Corti Auditory
outer hair cell of organ of Corti Type I hair cell of vestibular
apparatus of ear (acceleration and gravity) Type II hair cell of
vestibular apparatus of ear (acceleration and gravity) Type I taste
bud cell Olfactory receptor neuron Basal cell of olfactory
epithelium (stem cell for olfactory neurons) Type I carotid body
cell (blood pH sensor) Type II carotid body cell (blood pH sensor)
Merkel cell of epidermis (touch sensor) Touch-sensitive primary
sensory neurons (various types) Cold-sensitive primary sensory
neurons Heat-sensitive primary sensory neurons Pain-sensitive
primary sensory neurons (various types) Proprioceptive primary
sensory neurons (various types) Autonomic neuron cells Cholinergic
neural cell (various types) Adrenergic neural cell (various types)
Peptidergic neural cell (various types) Sense organ and peripheral
neuron supporting cells Inner pillar cell of organ of Corti Outer
pillar cell of organ of Corti Inner phalangeal cell of organ of
Corti Outer phalangeal cell of organ of Corti Border cell of organ
of Corti Hensen cell of organ of Corti Vestibular apparatus
supporting cell Type I taste bud supporting cell Olfactory
epithelium supporting cell Schwann cell Satellite cell
(encapsulating peripheral nerve cell bodies) Enteric glial cell
Central nervous system neurons and glial cells Neuron cells (large
variety of types, still poorly classified) Astrocyte (various
types) Oligodendrocyte Lens cells Anterior lens epithelial cell
Crystallin-containing lens fiber cell
Pigment cells Melanocyte Retinal pigmented epithelial cell Germ
cells Oogonium/Oocyte Spermatid Spermatocyte Spermatogonium cell
(stem cell for spermatocyte) Spermatozoon Nurse cells Ovarian
follicle cell Sertoli cell (in testis) Thymus epithelial cell
TABLE-US-00014 TABLE 6 Human Tissues Epithelial Tissue Connective
Tissues Unilaminar (simple) epithelia Fluid Connective Tissues
Squamous Lymph Cuboidal Blood Columnar Connective Tissues Proper
Sensory Loose Connective Tissues Myoepitheliocyte Areolar
Multilaminar eipithelia Loose Connective Tissues and Inflammation
Replacing or stratified squamous epithelia Adipose Stratified
cuboidal and columnar eipithelia Reticular Urothelium (transitional
epithelium) Dense Connective Tissues Seminiferous eipthelium
Regular(collagen) Glands Irregular(collagen) Exocrine glands
Regular(elastic) Ducts and Tubules Supportive Connective Tissues
Endocrine glands Osseous Tissue Nervous Tissue Compact Neurons
Cancellous Multipolar Neurons in CNS Cartilage Nerves Hyaline
Nerves of the PNS Elastic Receptors Fibrocartilage Miessner's and
Pacinian Corpuscles Muscle Tissue Non-striated Smooth Muscle
Striated Skeletal Muscle Cardiac Muscle Systems Major Structures
Skeletal Bones, cartilage, tendons, ligaments, and joints Muscular
Muscles (skeletal, cardiac, and smooth) Integumentary Skin, hair
nails, breast Circulatory Heart, blood vessels, blood Respiratory
Trachea, air passages, lungs Immune Lymph nodes and vessels, white
blood cells Digestive Mouth, esophagus, stomach, liver, pancreas,
duodenum, jejunum, ileum, caecum, rectum, gallbladder, pancreas,
small and large intestines Excretory and Urinary Kidneys, ureters,
bladder, urethra Nervous Brain, spinal cord, nerves, sense organs,
receptors, dorsal root ganglion Endocrine Endocrine glands, pineal
gland, pituitary gland, adrenal gland, thyroid gland, and hormones
Lymphatic Lymph nodes, spleen, lymph vessels Reproductive Ovaries,
uterus, fallopian tube, mammary glands (in females), vas deferens,
prostate, testes (in males), umbilical cord, placenta Functions
provides structure; supports and protects internal organs provides
structure; supports and moves trunk and limbs; moves substances
through body protects against pathogens; helps regulate body
temperature transports nutrients and wastes to and from all body
tissues carries air into and out of lungs, where gases (oxygen and
carbon dioxide) are exchanged provides protection against infection
and disease stores and digests food; absorbs nutrients; eliminates
waste eliminate waste; maintains water and chemical balance
controls and coordinates body movements and senses; controls
consciousness and creativity; helps monitor and maintain other body
systems maintain homeostasis; regulates metabolism, water and
mineral balance, growth and sexual development, and reproduction
cleans and returns tissue fluid to the blood and destroys pathogens
that enter the body produce gametes and offspring
TABLE-US-00015 TABLE 7 Cells of the Liver Hepatocytes
Perisinusoidal (Ito) cells Endotheliocytes Macrophages (Kupffer
cells) Lymphocytes (pit cells) Cells of the biliary tree Cuboldal
epitheliocytes Columnar epitheliocytes Connective tissue cells
TABLE-US-00016 TABLE 15 Adipocyte-myocyte reactions Reaction
Protein Abbreviation Reaction Name Equation Subsystem
Classification G6PASEer_ac glucose-6-phosphatase [f]: g6p + h2o
--> glc-D + pi Glycolysis/Gluconeogenesis EC-3.1.3.9 G6PASEer_mc
glucose-6-phosphatase [u]: g6p + h2o --> glc-D + pi
Glycolysis/Gluconeogenesis EC-3.1.3.9 PFK26_ac
6-phosphofructo-2-kinase [a]: atp + f6p --> adp + f26bp + h
Glycolysis/Gluconeogenesis EC-2.7.1.105 PGI_ac glucose-6-phosphate
[a]: g6p <==> f6p Glycolysis/Gluconeogenesis EC-5.3.1.9
isomerase PGK_ac phosphoglycerate kinase [a]: 13dpg + adp
<==> 3pg + atp Glycolysis/Gluconeogenesis EC-2.7.2.3 PGM_ac
phosphoglycerate mutase [a]: 3pg <==> 2pg
Glycolysis/Gluconeogenesis EC-5.4.2.1 PYK_ac pyruvate kinase [a]:
adp + h + pep --> atp + pyr Glycolysis/Gluconeogenesis
EC-2.7.1.40 TPI_ac triose-phosphate [a]: dhap <==> g3p
Glycolysis/Gluconeogenesis EC-5.3.1.1 isomerase ACONTm_ac Aconitate
hydratase [b]: cit <==> icit Central Metabolism EC-4.2.1.3
ACONTm_mc Aconitate hydratase [z]: cit <==> icit Central
Metabolism EC-4.2.1.3 AKGDm_ac 2-oxoglutarate [b]: akg + coa + nad
--> co2 + Central Metabolism dehydrogenase, nadh + succoa
mitochondrial AKGDm_mc 2-oxoglutarate [z]: akg + coa + nad -->
co2 + Central Metabolism dehydrogenase, nadh + succoa mitochondrial
CITL2_ac Citrate lyase (ATP- [a]: atp + cit + coa --> accoa +
adp + Central Metabolism EC-4.1.3.8 requiring) oaa + pi CITL2_mc
Citrate lyase (ATP- [y]: atp + cit + coa --> accoa + adp +
Central Metabolism EC-4.1.3.8 requiring) oaa + pi CSm_ac citrate
synthase [b]: accoa + h2o + oaa --> cit + Central Metabolism
EC-4.1.3.7 coa + h CSm_mc citrate synthase [z]: accoa + h2o + oaa
--> cit + Central Metabolism EC-4.1.3.7 coa + h ENO_ac enolase
[a]: 2pg <==> h2o + pep Central Metabolism EC-4.2.1.11 ENO_mc
enolase [y]: 2pg <==> h2o + pep Central Metabolism
EC-4.2.1.11 FBA_ac fructose-bisphosphate [a]: fdp <==> dhap +
g3p Central Metabolism EC-4.1.2.13 aldolase FBA_mc
fructose-bisphosphate [y]: fdp <==> dhap + g3p Central
Metabolism EC-4.1.2.13 aldolase FBP26_ac Fructose-2,6-bisphosphate
[a]: f26bp + h2o --> f6p + pi Central Metabolism EC-3.1.3.46
2-phosphatase FBP26_mc Fructose-2,6-bisphosphate [y]: f26bp + h2o
--> f6p + pi Central Metabolism EC-3.1.3.46 2-phosphatase FBP_ac
fructose-bisphosphatase [a]: fdp + h2o --> f6p + pi Central
Metabolism EC-3.1.3.11 FBP_mc fructose-bisphosphatase [y]: fdp +
h2o --> f6p + pi Central Metabolism EC-3.1.3.11 FUMm_ac
fumarase, mitochondrial [b]: fum + h2o <==> mal-L Central
Metabolism EC-4.2.1.2 FUMm_mc fumarase, mitochondrial [z]: fum +
h2o <==> mal-L Central Metabolism EC-4.2.1.2 G3PD1_ac
glycerol-3-phosphate [a]: glyc3p + nad <==> dhap + h +
Central Metabolism EC-1.1.1.94 dehydrogenase (NAD), nadh adipocyte
G3PD_mc Glycerol-3-phosphate [y]: dhap + h + nadh --> glyc3p +
Central Metabolism EC-1.1.1.8 dehydrogenase (NAD) nad G3PDm_ac
glycerol-3-phosphate [b]: fad + glyc3p --> dhap + fadh2 Central
Metabolism EC-1.1.99.5 dehydrogenase G3PDm_mc glycerol-3-phosphate
[z]: fad + glyc3p --> dhap + fadh2 Central Metabolism
EC-1.1.99.5 dehydrogenase G6PDH_ac glucose 6-phosphate [a]: g6p +
nadp --> 6pgl + h + Central Metabolism EC-1.1.1.49 dehydrogenase
nadph G6PDH_mc glucose 6-phosphate [y]: g6p + nadp --> 6pgl + h
+ Central Metabolism EC-1.1.1.49 dehydrogenase nadph GAPD_ac
glyceraldehyde-3- [a]: g3p + nad + pi <==> 13dpg + Central
Metabolism EC-1.2.1.12 phosphate dehydrogenase h + nadh (NAD)
GAPD_mc glyceraldehyde-3- [y]: g3p + nad + pi <==> 13dpg +
Central Metabolism EC-1.2.1.12 phosphate dehydrogenase h + nadh
(NAD) GL3Ptm_ac glycerol-3-phosphate glyc3p[a] <==> glyc3p[b]
Central Metabolism transport, adipocyte mitochondrial GLCP_ac
glycogen phosphorylase [a]: glycogen + pi --> g1p Central
Metabolism EC-2.4.1.1 HCO3Em_ac HCO3 equilibration [b]: co2 + h2o
<==> h + hco3 Central Metabolism EC-4.2.1.1 reaction,
mitochondrial HCO3Em_mc HCO3 equilibration [z]: co2 + h2o
<==> h + hco3 Central Metabolism EC-4.2.1.1 reaction,
mitochondrial HEX1_ac hexokinase (D- [a]: atp + glc-D --> adp +
g6p + h Central Metabolism EC-2.7.1.2 glucose:ATP) HEX1_mc
hexokinase (D- [y]: atp + glc-D --> adp + g6p + h Central
Metabolism EC-2.7.1.2 glucose:ATP) ICDHxm_ac Isocitrate
dehydrogenase [b]: icit + nad --> akg + co2 + nadh Central
Metabolism EC-1.1.1.41 (NAD+) ICDHxm_mc Isocitrate dehydrogenase
[z]: icit + nad --> akg + co2 + nadh Central Metabolism
EC-1.1.1.41 (NAD+) ICDHym_ac Isocitrate dehydrogenase [b]: icit +
nadp --> akg + co2 + Central Metabolism EC-1.1.1.42 (NADP+)
nadph ICDHym_mc Isocitrate dehydrogenase [z]: icit + nadp -->
akg + co2 + Central Metabolism EC-1.1.1.42 (NADP+) nadph LDH_L_mc
L-lactate dehydrogenase [y]: lac-L + nad <==> h + nadh +
Central Metabolism EC-1.1.1.27 pyr MDH_ac malate dehydrogenase [a]:
mal-L + nad <==> h + nadh + Central Metabolism EC-1.1.1.37
oaa MDH_mc malate dehydrogenase [y]: mal-L + nad <==> h +
nadh + Central Metabolism EC-1.1.1.37 oaa MDHm_ac malate
dehydrogenase, [b]: mal-L + nad <==> h + nadh + Central
Metabolism EC-1.1.1.37 mitochondrial oaa MDHm_mc malate
dehydrogenase, [z]: mal-L + nad <==> h + nadh + Central
Metabolism EC-1.1.1.37 mitochondrial oaa ME1m_ac malic enzyme
(NAD), [b]: mal-L + nad --> co2 + nadh + Central Metabolism
EC-1.1.1.38 mitochondrial pyr ME1m_mc malic enzyme (NAD), [z]:
mal-L + nad --> co2 + nadh + Central Metabolism EC-1.1.1.38
mitochondrial pyr ME2_ac malic enzyme (NADP) [a]: mal-L + nadp
--> co2 + nadph + Central Metabolism EC-1.1.1.40 pyr ME2_mc
malic enzyme (NADP) [y]: mal-L + nadp --> co2 + nadph + Central
Metabolism EC-1.1.1.40 pyr ME2m_ac malic enzyme (NADP), [b]: mal-L
+ nadp --> co2 + nadph + Central Metabolism EC-1.1.1.40
mitochondrial pyr ME2m_mc malic enzyme (NADP), [z]: mal-L + nadp
--> co2 + nadph + Central Metabolism EC-1.1.1.40 mitochondrial
pyr PCm_mc pyruvate carboxylase, [z]: atp + hco3 + pyr --> adp +
h + Central Metabolism EC-6.4.1.1 mitochondrial oaa + pi PDHm_mc
pyruvate dehydrogenase, [z]: coa + nad + pyr --> accoa + Central
Metabolism EC-1.2.1.51 mitochondrial co2 + nadh PFK26_mc
6-phosphofructo-2-kinase [y]: atp + f6p --> adp + f26bp + h
Central Metabolism EC-2.7.1.105 PFK_ac phosphofructokinase [a]: atp
+ f6p --> adp + fdp + h Central Metabolism EC-2.7.1.11 PFK_mc
phosphofructokinase [y]: atp + f6p --> adp + fdp + h Central
Metabolism EC-2.7.1.11 PGDH_mc phosphogluconate [y]: 6pgc + nadp
--> co2 + nadph + Central Metabolism EC-1.1.1.44 dehydrogenase
ru5p-D PGI_mc glucose-6-phosphate [y]: g6p <==> f6p Central
Metabolism EC-5.3.1.9 isomerase PGK_mc phosphoglycerate kinase [y]:
13dpg + adp <==> 3pg + atp Central Metabolism EC-2.7.2.3
PGL_mc 6- [y]: 6pgl + h2o --> 6pgc + h Central Metabolism
EC-3.1.1.31 phosphogluconolactonase PGM_mc phosphoglycerate mutase
[y]: 3pg <==> 2pg Central Metabolism EC-5.4.2.1 PPA_ac
inorganic diphosphatase [a]: h2o + ppi --> h + (2) pi Central
Metabolism EC-3.6.1.1 PPA_mc inorganic diphosphatase [y]: h2o + ppi
--> h + (2) pi Central Metabolism EC-3.6.1.1 PPCKG_ac
phosphoenolpyruvate [a]: gtp + oaa --> co2 + gdp + pep Central
Metabolism EC-4.1.1.32 carboxykinase (GTP) PPCKG_mc
phosphoenolpyruvate [y]: gtp + oaa --> co2 + gdp + pep Central
Metabolism EC-4.1.1.32 carboxykinase (GTP) PYK_mc pyruvate kinase
[y]: adp + h + pep --> atp + pyr Central Metabolism EC-2.7.1.40
RPE_mc ribulose 5-phosphate 3- [y]: ru5p-D <==> xu5p-D
Central Metabolism EC-5.1.3.1 epimerase RPI_mc ribose-5-phosphate
[y]: r5p <==> ru5p-D Central Metabolism EC-5.3.1.6 isomerase
SUCD1m_mc succinate dehydrogenase [z]: succ + ubq <==> fum +
qh2 Central Metabolism EC-1.3.5.1 SUCD3m_mc succinate dehydrogenase
[z]: fadh2 + ubq <==> fad + qh2 Central Metabolism cytochrome
b SUCOASAm_mc Succinate--CoA ligase [z]: atp + coa + succ
<==> adp + Central Metabolism EC-6.2.1.4 (ADP-forming) pi +
succoa SUCOASGm_mc Succinate--CoA ligase [z]: coa + gtp + succ
<==> gdp + Central Metabolism EC-6.2.1.4 (GDP-forming) pi +
succoa TAL_mc transaldolase [y]: g3p + s7p <==> e4p + f6p
Central Metabolism EC-2.2.1.2 TKT1_mc transketolase [y]: r5p +
xu5p-D <==> g3p + s7p Central Metabolism EC-2.2.1.1 TKT2_mc
transketolase [y]: e4p + xu5p-D <==> f6p + g3p Central
Metabolism EC-2.2.1.1 TPI_mc triose-phosphate [y]: dhap <==>
g3p Central Metabolism EC-5.3.1.1 isomerase SUCOASAm_ac
Succinate--CoA ligase [b]: atp + coa + succ <==> adp +
Citrate Cycle (TCA) EC-6.2.1.4 (ADP-forming) pi + succoa
SUCOASGm_ac Succinate--CoA ligase [b]: coa + gtp + succ <==>
gdp + Citrate Cycle (TCA) EC-6.2.1.4 (GDP-forming) pi + succoa
PGDH_ac phosphogluconate [a]: 6pgc + nadp --> co2 + nadph +
Pentose Phosphate EC-1.1.1.44 dehydrogenase ru5p-D Cycle PGL_ac 6-
[a]: 6pgl + h2o --> 6pgc + h Pentose Phosphate EC-3.1.1.31
phosphogluconolactonase Cycle RPE_ac ribulose 5-phosphate 3- [a]:
ru5p-D <==> xu5p-D Pentose Phosphate EC-5.1.3.1 epimerase
Cycle RPI_ac ribose-5-phosphate [a]: r5p <==> ru5p-D Pentose
Phosphate EC-5.3.1.6 isomerase Cycle TAL_ac transaldolase [a]: g3p
+ s7p <==> e4p + f6p Pentose Phosphate EC-2.2.1.2 Cycle
TKT1_ac transketolase [a]: r5p + xu5p-D <==> g3p + s7p
Pentose Phosphate EC-2.2.1.1 Cycle TKT2_ac transketolase [a]: e4p +
xu5p-D <==> f6p + g3p Pentose Phosphate EC-2.2.1.1 Cycle
PCm_ac pyruvate carboxylase, [b]: atp + hco3 + pyr --> adp + h +
Pyruvate metabolism EC-6.4.1.1 mitochondrial oaa + pi PDHm_ac
pyruvate dehydrogenase, [b]: coa + nad + pyr --> accoa +
Pyruvate metabolism EC-1.2.1.51 mitochondrial co2 + nadh ATPM_ac
ATP maintenance [a]: atp + h2o --> adp + h + pi Energy
Metabolism requirment ATPM_mc ATP maintenance [y]: atp + h2o -->
adp + h + pi Energy Metabolism requirment ATPS4m_ac ATP synthase,
adipocyte adp[b] + (4) h[a] + pi[b] --> atp[b] + Energy
Metabolism EC-3.6.1.14, mitochondrial (3) h[b] + h2o[b] ATPS4m_mc
ATP synthase, myocyte adp[z] + (4) h[y] + pi[z] --> atp[z] +
Energy Metabolism EC-3.6.1.14, mitochondrial (3) h[z] + h2o[y]
ATPSis_ac ATPase, adipocyte atp[a] + h2o[a] --> adp[a] + h[i] +
Energy Metabolism EC-3.6.3.6, cytosolic pi[a] ATPSis_mc ATPase,
myocyte atp[y] + h2o[y] --> adp[y] + h[c] + Energy Metabolism
EC-3.6.3.6, cytosolic pi[y] CREATK_mc creatine kinase, myocyte [y]:
atp + creat <==> adp + creatp Energy Metabolism EC-2.7.3.2
cytosol CREATPD_mc creatine phosphate [y]: creatp --> crtn + h +
pi Energy Metabolism dephosphorylation, spontaneous CYOO4m_ac
cytochrome c oxidase (4) focytc[b] + (8) h[b] + o2[b] --> Energy
Metabolism EC-1.9.3.1, (adipocyte mitochondrial 4 (4) ficytc[b] +
(4) h[a] + (2) h2o[b] protons) CYOO4m_mc cytochrome c oxidase (4)
focytc[z] + (8) h[z] + o2[z] --> Energy Metabolism EC-1.9.3.1,
(myocyte mitochondrial 4 (4) ficytc[z] + (4) h[y] + (2) h2o[z]
protons) CYOR4m_ac ubiquinol cytochrome c (2) ficytc[b] + (2) h[b]
+ qh2[b] --> Energy Metabolism EC-1.10.2.2, reductase, adipocyte
(2) focytc[b] + (4) h[a] + ubq[b] CYOR4m_mc ubiquinol cytochrome c
(2) ficytc[z] + (2) h[z] + qh2[z] --> Energy Metabolism
EC-1.10.2.2, reductase, myocyte (2) focytc[z] + (4) h[y] + ubq[z]
NADH4m_mc NADH dehydrogenase, (5) h[z] + nadh[z] + ubq[z] -->
(4) Energy Metabolism EC-1.6.99.3, mitochondrial h[y] + nad[z] +
qh2[z] NADH4m_ac NADH dehydrogenase, (5) h[b] + nadh[b] + ubq[b]
--> (4) Oxidative EC-1.6.99.3, adipocyte mitochondrial h[a] +
nad[b] + qh2[b] phosphorylation SUCD1m_ac succinate dehydrogenase
[b]: succ + ubq <==> fum + qh2 Oxidative EC-1.3.5.1
phosphorylation SUCD3m_ac succinate dehydrogenase [b]: fadh2 + ubq
<==> fad + qh2 Oxidative cytochrome b phosphorylation
GALUi_ac UTP-glucose-1-phosphate [a]: g1p + h + utp --> ppi +
udpg Galactose metabolism EC-2.7.7.9 uridylyltransferase
(irreversible) PGMT_ac phosphoglucomutase [a]: g1p <==> g6p
Galactose metabolism EC-5.4.2.2 GALUi_mc UTP-glucose-1-phosphate
[y]: g1p + h + utp --> ppi + udpg Carbohydrate EC-2.7.7.9
uridylyltransferase Metabolism (irreversible) GLCP_mc glycogen
phosphorylase [y]: glycogen + pi --> g1p Carbohydrate EC-2.4.1.1
Metabolism GLYGS_ac glycogen synthase [a]: udpg --> glycogen + h
+ udp Carbohydrate EC-2.4.1.11 (UDPGlc) Metabolism GLYGS_mc
glycogen synthase [y]: udpg --> glycogen + h + udp Carbohydrate
EC-2.4.1.11 (UDPGlc) Metabolism PGMT_mc phosphoglucomutase [y]: g1p
<==> g6p Carbohydrate EC-5.4.2.2 Metabolism ACACT10m_ac
acetyl-CoA C- [b]: 2maacoa + coa --> accoa + Amino Acid
Metabolism EC-2.3.1.16 acyltransferase, adipocyte ppcoa
mitochondrial ACOAD3m_ac acyl-CoA dehydrogenase, [b]: 2mbcoa + fad
<==> 2mb2coa + Amino Acid Metabolism EC-1.3.99.3 adipocyte
mitochondrial fadh2 ASPO_D_ac D-aspartate oxidase [a]: asp-D + h2o
+ o2 --> h + h2o2 + Amino Acid Metabolism EC-1.4.3.16 nh3 + oaa
ASPR_ac aspartase racemase, [a]: asp-D <==> asp-L Amino Acid
Metabolism EC-5.1.1.13 adipocyte cytosolic ASPTA1_ac aspartate
transaminase [a]: akg + asp-L <==> glu-L + oaa Amino Acid
Metabolism EC-2.6.1.1 ASPTA1_mc aspartate transaminase [y]: akg +
asp-L <==> glu-L + oaa Amino Acid Metabolism EC-2.6.1.1
ASPTA1m_ac aspartate transaminase, [b]: akg + asp-L <==>
glu-L + oaa Amino Acid Metabolism EC-2.6.1.1 mitochondrial
ASPTA1m_mc aspartate transaminase, [z]: akg + asp-L <==>
glu-L + oaa Amino Acid Metabolism EC-2.6.1.1 mitochondrial
ECOAH3m_ac enoyl-CoA hydratase, [b]: 2mb2coa + h2o <==> Amino
Acid Metabolism EC-4.2.1.17 adipocyte mitochondrial 3hmbcoa
HACD8m_ac 3-hydroxyacyl-CoA [b]: 3hmbcoa + nad <==> Amino
Acid Metabolism EC-1.1.1.35 dehydrogenase (2- 2maacoa + h + nadh
Methylacetoacetyl-CoA), adipocyte mitochondrial ILETA_ac isoleucine
transaminase, [a]: akg + ile-L <==> 3mop + glu-L Amino Acid
Metabolism EC-2.6.1.42 adipocyte cytosolic MOBD3m_ac
3-Methyl-2-oxobutanoate [b]: 3mop + coa + nad --> 2mbcoa + Amino
Acid Metabolism dehydrogenase, adipocyte co2 + nadh mitochondrial
CSNAT_mc carnitine O- [y]: accoa + crn --> acrn + coa Carnitine
Shuttle EC-2.3.1.7 acetyltransferase, myocyte cytosol CSNATifm_mc
carnitine O- [z]: acrn + coa --> accoa + crn Carnitine Shuttle
EC-2.3.1.7 aceyltransferase, forward reaction, myocyte
mitochondrial PPS_ac propionyl-CoA synthetase, [a]: atp + coa + ppa
<==> amp + Propanoate EC-6.2.1.1 adipocyte cytosolic ppcoa +
ppi Metabolism PPSm_ac propionyl-CoA synthetase, [b]: atp + coa +
ppa <==> amp + Propanoate EC-6.2.1.1 adipocyte mitochondrial
ppcoa + ppi Metabolism ACACT10m_mc acetyl-CoA C- [z]: accoa + occoa
<==> 3odcoa + Fatty Acid Degradation EC-2.3.1.16
acyltransferase (octanoyl- coa CoA) ACACT11m_mc acetyl-CoA C- [z]:
accoa + nncoa <==> 3oedcoa + Fatty Acid Degradation
EC-2.3.1.16 acyltransferase (nonanoyl- coa CoA) ACACT12m_mc
acetyl-CoA C- [z]: accoa + dccoa <==> 3oddcoa + Fatty Acid
Degradation EC-2.3.1.16 acyltransferase (decanoyl- coa CoA)
ACACT13m_mc acetyl-CoA C- [z]: accoa + edcoa <==> 3otrdcoa +
Fatty Acid Degradation EC-2.3.1.16 acyltransferase coa
(endecanoyl-CoA) ACACT145m_mc acetyl-CoA C- [z]: accoa + cis-dd2coa
<==> Fatty Acid Degradation EC-2.3.1.16 acyltransferase
3otdecoa5 + coa (dodecenoyl-CoA C12:1CoA, n-3) ACACT14m_mc
acetyl-CoA C- [z]: accoa + ddcoa <==> 3otdcoa + Fatty Acid
Degradation EC-2.3.1.16 acyltransferase coa (dodecanoyl-CoA)
ACACT15m_mc acetyl-CoA C- [z]: accoa + trdcoa <==> 3opdcoa +
Fatty Acid Degradation EC-2.3.1.16 acyltransferase coa
(tridecanoyl-CoA) ACACT167m_mc acetyl-CoA C- [z]: accoa + tdecoa5
<==> Fatty Acid Degradation EC-2.3.1.16 acyltransferase
3ohdecoa7 + coa (tetradecenoyl-CoA C14:1CoA, n-5) ACACT16m_mc
acetyl-CoA C- [z]: accoa + tdcoa <==> 3ohdcoa + Fatty Acid
Degradation EC-2.3.1.16 acyltransferase coa (tetradecanoyl-CoA)
ACACT189m_mc acetyl-CoA C- [z]: accoa + hdcoa7 <==> Fatty
Acid Degradation EC-2.3.1.16 acyltransferase 3oodcecoa9 + coa
(hexadecenoyl-CoA C16:1CoA, n-7) ACACT18m_mc acetyl-CoA C- [z]:
accoa + pmtcoa <==> Fatty Acid Degradation EC-2.3.1.16
acyltransferase (palmitoyl- 3oodcoa + coa CoA C16:0CoA) ACACT20m_mc
acetyl-CoA C- [z]: accoa + strcoa <==> 3oescoa + Fatty Acid
Degradation EC-2.3.1.16 acyltransferase coa (octadecanoyl-CoA
C18:0CoA) ACACT22p_mc acetyl-CoA C- [w]: accoa + ecsacoa <==>
Fatty Acid Degradation EC-2.3.1.16 acyltransferase 3odscoa + coa
(eicosanoyl-CoA C20:0CoA) ACACT4m_mc acetyl-CoA C- [z]: (2) accoa
<==> aacoa + coa Fatty Acid Degradation EC-2.3.1.16
acyltransferase (acetyl- CoA) ACACT5m_mc acetyl-CoA C- [z]: accoa +
ppcoa <==> 3optcoa + Fatty Acid Degradation EC-2.3.1.16
acyltransferase (propanoyl- coa CoA) ACACT6m_mc acetyl-CoA C- [z]:
accoa + btcoa <==> 3ohcoa + Fatty Acid Degradation
EC-2.3.1.16 acyltransferase (butanoyl- coa CoA) ACACT7m_mc
acetyl-CoA C- [z]: accoa + ptcoa <==> 3ohpcoa + Fatty Acid
Degradation EC-2.3.1.16 acyltransferase (pentanoyl- coa CoA)
ACACT8m_mc acetyl-CoA C- [z]: accoa + hxcoa <==> 3oocoa +
Fatty Acid Degradation EC-2.3.1.16 acyltransferase (hexanoyl- coa
CoA) ACACT9m_mc acetyl-CoA C- [z]: accoa + hpcoa <==> 3onncoa
+ Fatty Acid Degradation EC-2.3.1.16 acyltransferase (heptanoyl-
coa CoA) ACOAD10m_mc acyl-CoA dehydrogenase [z]: dccoa + fad
<==> dc2coa + Fatty Acid Degradation EC-1.3.99.13
(decanoyl-CoA C10:0CoA) fadh2 ACOAD11m_mc acyl-CoA dehydrogenase
[z]: edcoa + fad <==> ed2coa + Fatty Acid Degradation
EC-1.3.99.13 (endecanoyl-CoA) fadh2 ACOAD12m_mc acyl-CoA
dehydrogenase [z]: ddcoa + fad <==> fadh2 + Fatty Acid
Degradation EC-1.3.99.13 (dodecanoyl-CoA trans-dd2coa C12:0CoA)
ACOAD13m_mc acyl-CoA dehydrogenase [z]: fad + trdcoa <==>
fadh2 + Fatty Acid Degradation EC-1.3.99.13 (tridecanoyl-CoA)
trd2coa ACOAD145m_mc acyl-CoA dehydrogenase [z]: fad + tdecoa5
<==> fadh2 + Fatty Acid Degradation EC-1.3.99.13
(tetradecenoyl-CoA, tde2coa5 C14:1CoA, n-5) ACOAD14m_mc acyl-CoA
dehydrogenase [z]: fad + tdcoa <==> fadh2 + Fatty Acid
Degradation EC-1.3.99.13 (tetradecanoyl-CoA) td2coa ACOAD15m_mc
acyl-CoA dehydrogenase [z]: fad + pdcoa <==> fadh2 + Fatty
Acid Degradation EC-1.3.99.13 (pentadecanoyl-CoA) pd2coa
ACOAD167m_mc acyl-CoA dehydrogenase [z]: fad + hdcoa7 <==>
fadh2 + Fatty Acid Degradation EC-1.3.99.13 (hexadecenoyl-CoA,
hde2coa7 C16:1CoA, n-7) ACOAD16m_mc acyl-CoA dehydrogenase [z]: fad
+ pmtcoa <==> fadh2 + Fatty Acid Degradation EC-1.3.99.13
(hexadecanoyl-CoA hdd2coa C16:0CoA) ACOAD189m_mc acyl-CoA
dehydrogenase [z]: fad + odecoa9 <==> fadh2 + Fatty Acid
Degradation EC-1.3.99.13 (octadecenoyl-CoA, ode2coa9 C18:1CoA, n-9)
ACOAD18m_mc acyl-CoA dehydrogenase [z]: fad + strcoa <==>
fadh2 + Fatty Acid Degradation EC-1.3.99.13 (Stearyl-CoA, C18:0CoA)
od2coa
ACOAD20m_mc acyl-CoA dehydrogenase [z]: ecsacoa + fad <==>
es2coa + Fatty Acid Degradation EC-1.3.99.13 (eicosanoyl-CoA, fadh2
C20:0CoA) ACOAD22p_mc acyl-CoA dehydrogenase [w]: dcsacoa + fad
<==> ds2coa + Fatty Acid Degradation EC-1.3.99.13
(docosanoyl-CoA, fadh2 C22:0CoA) ACOAD4m_mc acyl-CoA dehydrogenase
[z]: btcoa + fad <==> b2coa + Fatty Acid Degradation
EC-1.3.99.13 (butanoyl-CoA C4:0CoA) fadh2 ACOAD5m_mc acyl-CoA
dehydrogenase [z]: fad + ptcoa <==> fadh2 + Fatty Acid
Degradation EC-1.3.99.13 (pentanoyl-CoA) pt2coa ACOAD6m_mc acyl-CoA
dehydrogenase [z]: fad + hxcoa <==> fadh2 + Fatty Acid
Degradation EC-1.3.99.13 (hexanoyl-CoA C8:0CoA) hx2coa ACOAD7m_mc
acyl-CoA dehydrogenase [z]: fad + hpcoa <==> fadh2 + Fatty
Acid Degradation EC-1.3.99.13 (heptanoyl-CoA) hp2coa ACOAD8m_mc
acyl-CoA dehydrogenase [z]: fad + occoa <==> fadh2 + Fatty
Acid Degradation EC-1.3.99.13 (octanoyl-CoA C8:0CoA) oc2coa
ACOAD9m_mc acyl-CoA dehydrogenase [z]: fad + nncoa <==> fadh2
+ Fatty Acid Degradation EC-1.3.99.13 (nonanoyl-CoA) nn2coa
CRNDST_mc carnitine [y]: crn + dcsacoa --> coa + Fatty Acid
Degradation EC-2.3.1.21 docosanoyltransferase, dcsacrn myocyte
CRNDSTp_mc carnitine coa[w] + dcsacrn[y] <==> crn[y] + Fatty
Acid Degradation docosanoyltransferase II, dcsacoa[w] myocyte
CRNDT_mc carnitine [y]: crn + ddcoa <==> coa + ddcrn Fatty
Acid Degradation EC-2.3.1.21 dodecanoyltransferase, myocyte
CRNDTm_mc carnitine coa[z] + ddcrn[y] <==> crn[y] + Fatty
Acid Degradation dodecanoyltransferase II, ddcoa[z] myocyte
CRNET_mc carnitine [y]: crn + ecsacoa <==> coa + Fatty Acid
Degradation EC-2.3.1.21 eicosanoyltransferase, ecsacrn myocyte
CRNETm_mc carnitine coa[z] + ecsacrn[y] <==> crn[y] + Fatty
Acid Degradation eicosanoyltransferase II, ecsacoa[z] myocyte
CRNETp_mc carnitine coa[w] + ecsacrn[y] <==> crn[y] + Fatty
Acid Degradation eicosanoyltransferase II, ecsacoa[w] myocyte
CRNODET_mc carnitine 9-cis- [y]: crn + odecoa9 <==> coa +
Fatty Acid Degradation EC-2.3.1.21 octadecenoyltransferase, odecrn9
myocyte CRNOT_mc carnitine [y]: crn + strcoa <==> coa +
strcrn Fatty Acid Degradation EC-2.3.1.21 octadecanoyltransferase,
myocyte CRNOTm_mc carnitine coa[z] + strcrn[y] <==> crn[y] +
Fatty Acid Degradation octadecanoyltransferase strcoa[z] II,
myocyte CRNPTDT_mc carnitine [y]: crn + pdcoa <==> coa +
pdcrn Fatty Acid Degradation EC-2.3.1.21 pentadecanoyltransferase,
myocyte CRNPT_mc carnitine O- [y]: crn + pmtcoa --> coa + pmtcrn
Fatty Acid Degradation EC-2.3.1.21 palmitoyltransferase, myocyte
CRNPTm_mc carnitine O- coa[z] + pmtcrn[y] --> crn[y] + Fatty
Acid Degradation palmitoyltransferase II, pmtcoa[z] myocyte
CRNTT_mc carnitine [y]: crn + tdcoa <==> coa + tdcrn Fatty
Acid Degradation EC-2.3.1.21 tetradecanoyltransferase, myocyte
CRNTTm_mc carnitine coa[z] + tdcrn[y] <==> crn[y] + Fatty
Acid Degradation tetradecanoyltransferase tdcoa[z] II, myocyte
DDCIm_mc dodecenoyl-CoA D- [z]: cis-dd2coa <==> trans-dd2coa
Fatty Acid Degradation EC-5.3.3.8 isomerase, myocyte mitochondrial
ECOAH10m_mc 3-hydroxyacyl-CoA [z]: 3hdcoa <==> dc2coa + h2o
Fatty Acid Degradation EC-4.2.1.17 dehydratase (3-
hydroxydecanoyl-CoA) ECOAH11m_mc 3-hydroxyacyl-CoA [z]: 3hedcoa
<==> ed2coa + h2o Fatty Acid Degradation EC-4.2.1.17
dehydratase (3- hydroxyendecanoyl-CoA) ECOAH12m_mc
3-hydroxyacyl-CoA [z]: 3hddcoa <==> h2o + trans- Fatty Acid
Degradation EC-4.2.1.17 dehydratase (3- dd2coa
hydroxydodecanoyl-CoA) ECOAH13m_mc 3-hydroxyacyl-CoA [z]: 3htrdcoa
<==> h2o + trd2coa Fatty Acid Degradation EC-4.2.1.17
dehydratase (3- hydroxytridecanoyl-CoA) ECOAH145m_mc
3-hydroxyacyl-CoA [z]: 3htdecoa5 <==> h2o + Fatty Acid
Degradation EC-4.2.1.17 dehydratase (3- tde2coa5
hydroxytetradecenoyl- CoA, C14:1CoA, n-5) ECOAH14m_mc
3-hydroxyacyl-CoA [z]: 3htdcoa <==> h2o + td2coa Fatty Acid
Degradation EC-4.2.1.17 dehydratase (3- hydroxytetradecanoyl- CoA)
ECOAH15m_mc 3-hydroxyacyl-CoA [z]: 3hpdcoa <==> h2o + pd2coa
Fatty Acid Degradation EC-4.2.1.17 dehydratase (3-
hydroxypentadecanoyl- CoA) ECOAH167m_mc 3-hydroxyacyl-CoA [z]:
3hhdecoa7 <==> h2o + Fatty Acid Degradation EC-4.2.1.17
dehydratase (3- hde2coa7 hydroxyhexadecenoyl- CoA, C16:1CoA, n-7)
ECOAH16m_mc 3-hydroxyacyl-CoA [z]: 3hhdcoa <==> h2o + hdd2coa
Fatty Acid Degradation EC-4.2.1.17 dehydratase (3-
hydroxyhexadecanoyl- CoA) ECOAH189m_mc 3-hydroxyacyl-CoA [z]:
3hodecoa9 <==> h2o + Fatty Acid Degradation EC-4.2.1.17
dehydratase (3- ode2coa9 hydroxyoctadecenoyl-CoA, C18:1CoA, n-9)
ECOAH18m_mc 3-hydroxyacyl-CoA [z]: 3hodcoa <==> h2o + od2coa
Fatty Acid Degradation EC-4.2.1.17 dehydratase (3-
hydroxyoctadecanoyl-CoA, C18:0CoA) ECOAH20m_mc 3-hydroxyacyl-CoA
[z]: 3hescoa <==> es2coa + h2o Fatty Acid Degradation
EC-4.2.1.17 dehydratase (3- hydroxyeicosanoyl-CoA, C18:0CoA)
ECOAH22p_mc 3-hydroxyacyl-CoA [w]: 3hdscoa <==> ds2coa + h2o
Fatty Acid Degradation EC-4.2.1.17 dehydratase (3-
hydroxydocosanoyl-CoA, C18:0CoA) ECOAH4m_mc 3-hydroxyacyl-CoA [z]:
3hbycoa <==> b2coa + h2o Fatty Acid Degradation EC-4.2.1.17
dehydratase (3- hydroxybutanoyl-CoA) ECOAH5m_mc 3-hydroxyacyl-CoA
[z]: 3hptcoa <==> h2o + pt2coa Fatty Acid Degradation
EC-4.2.1.17 dehydratase (3- hydroxypentanoyl-CoA) ECOAH6m_mc
3-hydroxyacyl-CoA [z]: 3hhcoa <==> h2o + hx2coa Fatty Acid
Degradation EC-4.2.1.17 dehydratase (3- hydroxyhexanoyl-CoA)
ECOAH7m_mc 3-hydroxyacyl-CoA [z]: 3hhpcoa <==> h2o + hp2coa
Fatty Acid Degradation EC-4.2.1.17 dehydratase (3-
hydroxyheptanoyl-CoA) ECOAH8m_mc 3-hydroxyacyl-CoA [z]: 3hocoa
<==> h2o + oc2coa Fatty Acid Degradation EC-4.2.1.17
dehydratase (3- hydroxyoctanoyl-CoA) ECOAH9m_mc 3-hydroxyacyl-CoA
[z]: 3hnncoa <==> h2o + nn2coa Fatty Acid Degradation
EC-4.2.1.17 dehydratase (3- hydroxynonanoyl-CoA) HACD10m_mc
3-hydroxyacyl-CoA [z]: 3odcoa + h + nadh <==> Fatty Acid
Degradation EC-1.1.1.35 dehydrogenase (3- 3hdcoa + nad
oxodecanoyl-CoA) HACD11m_mc 3-hydroxyacyl-CoA [z]: 3oedcoa + h +
nadh <==> Fatty Acid Degradation EC-1.1.1.35 dehydrogenase
(3- 3hedcoa + nad oxoendecanoyl-CoA) HACD12m_mc 3-hydroxyacyl-CoA
[z]: 3oddcoa + h + nadh <==> Fatty Acid Degradation
EC-1.1.1.35 dehydrogenase (3- 3hddcoa + nad oxododecanoyl-CoA)
HACD13m_mc 3-hydroxyacyl-CoA [z]: 3otrdcoa + h + nadh <==>
Fatty Acid Degradation EC-1.1.1.35 dehydrogenase (3- 3htrdcoa + nad
oxotridecanoyl-CoA) HACD145m_mc 3-hydroxyacyl-CoA [z]: 3otdecoa5 +
h + nadh <==> Fatty Acid Degradation EC-1.1.1.35
dehydrogenase (3- 3htdecoa5 + nad oxotetradecenoyl-CoA C14:1CoA,
n-5) HACD14m_mc 3-hydroxyacyl-CoA [z]: 3otdcoa + h + nadh
<==> Fatty Acid Degradation EC-1.1.1.35 dehydrogenase (3-
3htdcoa + nad oxotetradecanoyl-CoA) HACD15m_mc 3-hydroxyacyl-CoA
[z]: 3opdcoa + h + nadh <==> Fatty Acid Degradation
EC-1.1.1.35 dehydrogenase (3- 3hpdcoa + nad oxopentadecanoyl-CoA)
HACD167m_mc 3-hydroxyacyl-CoA [z]: 3ohdecoa7 + h + nadh <==>
Fatty Acid Degradation EC-1.1.1.35 dehydrogenase (3- 3hhdecoa7 +
nad oxohexadecenoyl-CoA C16:1CoA, n-7) HACD16m_mc 3-hydroxyacyl-CoA
[z]: 3ohdcoa + h + nadh <==> Fatty Acid Degradation
EC-1.1.1.35 dehydrogenase (3- 3hhdcoa + nad oxohexadecanoyl-CoA)
HACD189m_mc 3-hydroxyacyl-CoA [z]: 3oodcecoa9 + h + nadh <==>
Fatty Acid Degradation EC-1.1.1.35 dehydrogenase (3- 3hodecoa9 +
nad oxooctadecenoyl-CoA C18:1CoA, n-9) HACD18m_mc 3-hydroxyacyl-CoA
[z]: 3oodcoa + h + nadh <==> Fatty Acid Degradation
EC-1.1.1.35 dehydrogenase (3- 3hodcoa + nad oxooctadecanoyl-CoA
C18:0CoA) HACD20m_mc 3-hydroxyacyl-CoA [z]: 3oescoa + h + nadh
<==> Fatty Acid Degradation EC-1.1.1.35 dehydrogenase (3-
3hescoa + nad oxoeicosanoyl-CoA C18:0CoA) HACD22p_mc
3-hydroxyacyl-CoA [z]: 3odscoa + h + nadh <==> Fatty Acid
Degradation EC-1.1.1.35 dehydrogenase (3- 3hdscoa + nad
oxodocosanoyl-CoA C18:0CoA) HACD4m_mc 3-hydroxyacyl-CoA [z]: aacoa
+ h + nadh <==> Fatty Acid Degradation EC-1.1.1.35
dehydrogenase (3- 3hbycoa + nad oxobutanoyl-CoA) HACD5m_mc
3-hydroxyacyl-CoA [z]: 3optcoa + h + nadh <==> Fatty Acid
Degradation EC-1.1.1.35 dehydrogenase (3- 3hptcoa + nad
oxopentanoyl-CoA) HACD6m_mc 3-hydroxyacyl-CoA [z]: 3ohcoa + h +
nadh <==> Fatty Acid Degradation EC-1.1.1.35 dehydrogenase
(3- 3hhcoa + nad oxohexanoyl-CoA) HACD7m_mc 3-hydroxyacyl-CoA [z]:
3ohpcoa + h + nadh <==> Fatty Acid Degradation EC-1.1.1.35
dehydrogenase (3- 3hhpcoa + nad oxoheptanoyl-CoA) HACD8m_mc
3-hydroxyacyl-CoA [z]: 3oocoa + h + nadh <==> Fatty Acid
Degradation EC-1.1.1.35
dehydrogenase (3- 3hocoa + nad oxooctanoyl-CoA) HACD9m_mc
3-hydroxyacyl-CoA [z]: 3onncoa + h + nadh <==> Fatty Acid
Degradation EC-1.1.1.35 dehydrogenase (3- 3hnncoa + nad
oxononanoyl-CoA) MMEm_mc methylmalonyl-CoA [z]: mmcoa-S <==>
mmcoa-R Fatty Acid Degradation EC-5.1.99.1 epimerase, myocyte
mitochondrial MMMm_mc R-methylmalonyl-CoA [z]: mmcoa-R -->
succoa Fatty Acid Degradation EC-5.4.99.2 mutase, myocyte
mitochondrial PPCOACm_mc Propionyl-CoA [z]: atp + hco3 + ppcoa
--> adp + h + Fatty Acid Degradation EC-6.4.1.3 carboxylase,
myocyte mmcoa-S + pi mitochondrial FACOAL120_mc fatty-acid--CoA
ligase [y]: atp + coa + ddca <==> amp + Fatty Acid Metabolism
EC-6.2.1.3 (dodecanoate, C12:0), ddcoa + ppi myocyte FACOAL140_mc
fatty-acid--CoA ligase [y]: atp + coa + ttdca <==> amp +
Fatty Acid Metabolism EC-6.2.1.3 (tetradecanoate, C14:0), ppi +
tdcoa myocyte FACOAL150_mc fatty-acid--CoA ligase [y]: atp + coa +
ptdca <==> amp + Fatty Acid Metabolism EC-6.2.1.3
(pentadecanoate, C15:0), pdcoa + ppi myocyte FACOAL160_mc
fatty-acid--CoA ligase [y]: atp + coa + hdca <==> amp + Fatty
Acid Metabolism EC-6.2.1.3 (hexadecanoate, C16:0), pmtcoa + ppi
myocyte FACOAL180_mc fatty-acid--CoA ligase [y]: atp + coa + ocdca
<==> amp + Fatty Acid Metabolism EC-6.2.1.3 (octadecanoate,
C28:0), ppi + strcoa myocyte FACOAL181_9_mc fatty-acid--CoA ligase
[y]: atp + coa + ocdcea9 <==> Fatty Acid Metabolism
EC-6.2.1.3 (octadecenoate, C18:1 n- amp + odecoa9 + ppi 9), myocyte
FACOAL200_mc fatty-acid--CoA ligase [y]: atp + coa + ecsa
<==> amp + Fatty Acid Metabolism EC-6.2.1.3 (eicosanoate,
C20:0), ecsacoa + ppi myocyte ACCOAC_ac acetyl-CoA carboxylase [a]:
accoa + atp + hco3 --> adp + h + Fatty Acid Synthesis EC-6.4.1.2
malcoa + pi AGAT_ac_HS_ub unbalanced 1-Acyl- [a]: 1ag3p_HS +
(0.00032) Fatty Acid Synthesis glycerol-3-phosphate dcsacoa +
(0.00698) ddcoa + acyltransferase, adipocyte (0.00024) dsecoa11 +
(0.00056) cytosol, Homo sapiens dsecoa9 + (0.00172) dshcoa3 +
specific (0.00163) dspcoa3 + (0.00016) dspcoa6 + (0.00182) ecsacoa
+ (0.00272) esdcoa6 + (0.00035) esdcoa9 + (0.00148) esecoa11 +
(0.00026) esecoa7 + (0.00732) esecoa9 + (0.00036) espcoa3 +
(0.00027) estcoa3 + (0.0023) estcoa6 + (0.00027) ettcoa3 +
(0.00311) ettcoa6 + (0.02985) hdcoa7 + (0.00582) hdcoa9 + (0.00295)
hpdcoa8 + (0.15761) ocdycacoa6 + (0.00499) odcoa3 + (0.00039)
odcoa6 + (0.0026) odecoa5 + (0.01831) odecoa7 + (0.39309) odecoa9 +
(0.00138) osttcoa6 + (0.00375) pdcoa + (0.24351) pmtcoa + (0.06379)
strcoa + (0.03728) tdcoa + (0.00244) tdecoa5 + (0.00037) tdecoa7
--> coa + pa_HS DESAT141_5_ac Myristicoyl-CoA [a]: h + nadph +
o2 + tdcoa --> (2) Fatty Acid Synthesis EC-1.14.19.1 desaturase
(n-C14:0CoA -> h2o + nadp + tdecoa5 C14:1CoA, n-5), adipocyte
DESAT141_7_ac Myristicoyl-CoA [a]: h + nadph + o2 + tdcoa -->
(2) Fatty Acid Synthesis EC-1.14.19.1 desaturase (n-C14:0CoA ->
h2o + nadp + tdecoa7 C14:1CoA, n-7), adipocyte DESAT161_7_ac
Palmitoyl-CoA desaturase [a]: h + nadph + o2 + pmtcoa --> Fatty
Acid Synthesis EC-1.14.19.1 (n-C16:0CoA -> (2) h2o + hdcoa7 +
nadp C16:1CoA, n-7), adipocyte DESAT161_9_ac Palmitoyl-CoA
desaturase [a]: h + nadph + o2 + pmtcoa --> Fatty Acid Synthesis
EC-1.14.19.1 (n-C16:0CoA -> (2) h2o + hdcoa9 + nadp C16:1CoA,
n-9), adipocyte DESAT171_8_ac Palmitoyl-CoA desaturase [a]: h +
hpdcoa + nadph + o2 --> Fatty Acid Synthesis EC-1.14.19.1
(n-C17:0CoA -> (2) h2o + hpdcoa8 + nadp C17:1CoA, n-8),
adipocyte DESAT181_5_ac stearoyl-CoA desaturase [a]: h + nadph + o2
+ strcoa --> (2) Fatty Acid Synthesis EC-1.14.19.1 (n-C18:0CoA
-> h2o + nadp + odecoa5 C18:1CoA, n-5), adipocyte DESAT181_7_ac
stearoyl-CoA desaturase [a]: h + nadph + o2 + strcoa --> (2)
Fatty Acid Synthesis EC-1.14.19.1 (n-C18:0CoA -> h2o + nadp +
odecoa7 C18:1CoA, n-7), adipocyte DESAT181_9_ac stearoyl-CoA
desaturase [a]: h + nadph + o2 + strcoa --> (2) Fatty Acid
Synthesis EC-1.14.19.1 (n-C18:0CoA -> h2o + nadp + odecoa9
C18:1CoA, n-9), adipocyte DESAT201_11_ac stearoyl-CoA desaturase
[a]: ecsacoa + h + nadph + o2 --> Fatty Acid Synthesis
EC-1.14.19.1 (n-C20:0CoA -> esecoa11 + (2) h2o + nadp C20:1CoA,
n-11), adipocyte DESAT201_7_ac stearoyl-CoA desaturase [a]: ecsacoa
+ h + nadph + o2 --> Fatty Acid Synthesis EC-1.14.19.1
(n-C20:0CoA -> esecoa7 + (2) h2o + nadp C20:1CoA, n-7),
adipocyte DESAT201_9_ac stearoyl-CoA desaturase [a]: ecsacoa + h +
nadph + o2 --> Fatty Acid Synthesis EC-1.14.19.1 (n-C20:0CoA
-> esecoa9 + (2) h2o + nadp C20:1CoA, n-9), adipocyte
DESAT202_9_ac stearoyl-CoA desaturase [a]: ecsacoa + (2) h + (2)
nadph + Fatty Acid Synthesis EC-1.14.19.1 (lumped: n-C20:0CoA ->
(2) o2 --> esdcoa9 + (4) h2o + (2) C20:2CoA, n-9), adipocyte
nadp DESAT221_11_ac stearoyl-CoA desaturase [a]: dcsacoa + h +
nadph + o2 --> Fatty Acid Synthesis EC-1.14.19.1 (n-C22:0CoA
-> dsecoa11 + (2) h2o + nadp C22:1CoA, n-11), adipocyte
DESAT221_9_ac stearoyl-CoA desaturase [a]: dcsacoa + h + nadph + o2
--> Fatty Acid Synthesis EC-1.14.19.1 (n-C22:0CoA -> dsecoa9
+ (2) h2o + nadp C22:1CoA, n-9), adipocyte FACOAL120_ac
fatty-acid--CoA ligase [a]: atp + coa + ddca <==> amp + Fatty
Acid Synthesis EC-6.2.1.3 (dodecanoate, C12:0), ddcoa + ppi
adipocyte FACOAL140_ac fatty-acid--CoA ligase [a]: atp + coa +
ttdca <==> amp + Fatty Acid Synthesis EC-6.2.1.3
(tetradecanoate, C14:0), ppi + tdcoa adipocyte FACOAL141_5_ac
fatty-acid--CoA ligase [a]: atp + coa + ttdcea5 <==> amp +
Fatty Acid Synthesis EC-6.2.1.3 (tetradecenoate, C14:1 n- ppi +
tdecoa5 5), adipocyte FACOAL141_7_ac fatty-acid--CoA ligase [a]:
atp + coa + ttdcea7 <==> amp + Fatty Acid Synthesis
EC-6.2.1.3 (tetradecenoate, C14:1 n- ppi + tdecoa7 7), adipocyte
FACOAL150_ac fatty-acid--CoA ligase [a]: atp + coa + ptdca
<==> amp + Fatty Acid Synthesis EC-6.2.1.3 (heptadecanoate,
C15:0), pdcoa + ppi adipocyte FACOAL160_ac fatty-acid--CoA ligase
[a]: atp + coa + hdca <==> amp + Fatty Acid Synthesis
EC-6.2.1.3 (hexadecanoate, C16:0), pmtcoa + ppi adipocyte
FACOAL161_7_ac fatty-acid--CoA ligase [a]: atp + coa + hdcea7
<==> amp + Fatty Acid Synthesis EC-6.2.1.3 (hexadecenoate,
C16:1 n- hdcoa7 + ppi 7), adipocyte FACOAL161_9_ac fatty-acid--CoA
ligase [a]: atp + coa + hdcea9 <==> amp + Fatty Acid
Synthesis EC-6.2.1.3 (hexadecenoate, C16:1 n- hdcoa9 + ppi 9),
adipocyte FACOAL170_ac fatty-acid--CoA ligase [a]: atp + coa +
hpdca <==> amp + Fatty Acid Synthesis EC-6.2.1.3
(heptadecanoate, C17:0), hpdcoa + ppi adipocyte FACOAL171_8_ac
fatty-acid--CoA ligase [a]: atp + coa + hpdcea8 <==> Fatty
Acid Synthesis EC-6.2.1.3 (heptadecenoate, C17:1 n- amp + hpdcoa8 +
ppi 8), adipocyte FACOAL180_ac fatty-acid--CoA ligase [a]: atp +
coa + ocdca <==> amp + Fatty Acid Synthesis EC-6.2.1.3
(octadecanoate, C18:0), ppi + strcoa adipocyte FACOAL181_5_ac
fatty-acid--CoA ligase [a]: atp + coa + ocdcea5 <==> Fatty
Acid Synthesis EC-6.2.1.3 (octadecenoate, C18:1 n- amp + odecoa5 +
ppi 5), adipocyte FACOAL181_7_ac fatty-acid--CoA ligase [a]: atp +
coa + ocdcea7 <==> Fatty Acid Synthesis EC-6.2.1.3
(octadecenoate, C18:1 n- amp + odecoa7 + ppi 7), adipocyte
FACOAL181_9_ac fatty-acid--CoA ligase [a]: atp + coa + ocdcea9
<==> Fatty Acid Synthesis EC-6.2.1.3 (octadecenoate, C18:1 n-
amp + odecoa9 + ppi 9), adipocyte FACOAL182_6_ac fatty-acid--CoA
ligase [a]: atp + coa + ocddea6 <==> Fatty Acid Synthesis
EC-6.2.1.3 (octadecadienoate, C18:2 amp + ocdycacoa6 + ppi n-6),
adipocyte FACOAL183_3_ac fatty-acid--CoA ligase [a]: atp + coa +
ocdctra3 <==> Fatty Acid Synthesis EC-6.2.1.3
(octadecadienoate, C18:3 amp + odcoa3 + ppi n-3), adipocyte
FACOAL183_6_ac fatty-acid--CoA ligase [a]: atp + coa + ocdctra6
<==> Fatty Acid Synthesis EC-6.2.1.3 (octadecadienoate, C18:3
amp + odcoa6 + ppi n-6), adipocyte FACOAL200_ac fatty-acid--CoA
ligase [a]: atp + coa + ecsa <==> amp + Fatty Acid Synthesis
EC-6.2.1.3 (eicosanoate, C20:0), ecsacoa + ppi adipocyte
FACOAL201_11_ac fatty-acid--CoA ligase [a]: atp + coa + ecsea11
<==> Fatty Acid Synthesis EC-6.2.1.3 (eicosenoate, C20:1
n-11), amp + esecoa11 + ppi adipocyte FACOAL201_7_ac
fatty-acid--CoA ligase [a]: atp + coa + ecsea7 <==> amp +
Fatty Acid Synthesis EC-6.2.1.3 (eicosenoate, C20:1 n-7), esecoa7 +
ppi adipocyte FACOAL201_9_ac fatty-acid--CoA ligase [a]: atp + coa
+ ecsea9 <==> amp + Fatty Acid Synthesis EC-6.2.1.3
(eicosenoate, C20:1 n-9), esecoa9 + ppi adipocyte FACOAL202_6_ac
fatty-acid--CoA ligase [a]: atp + coa + ecsdea6 <==> Fatty
Acid Synthesis EC-6.2.1.3 (eicosadienoate, C20:2 n- amp + esdcoa6 +
ppi 6), adipocyte FACOAL202_9_ac fatty-acid--CoA ligase [a]: atp +
coa + ecsdea9 <==> Fatty Acid Synthesis EC-6.2.1.3
(eicosadienoate, C20:2 n- amp + esdcoa9 + ppi 9), adipocyte
FACOAL203_3_ac fatty-acid--CoA ligase [a]: atp + coa + ecstea3
<==> amp + Fatty Acid Synthesis EC-6.2.1.3 (eicosatrienoate,
C20:3 n- estcoa3 + ppi 6), adipocyte FACOAL203_6_ac fatty-acid--CoA
ligase [a]: atp + coa + ecstea6 <==> amp + Fatty Acid
Synthesis EC-6.2.1.3 (eicosatrienoate, C20:3 n- estcoa6 + ppi 6),
adipocyte FACOAL204_3_ac fatty-acid--CoA ligase [a]: atp + coa +
ecsttea3 <==> Fatty Acid Synthesis EC-6.2.1.3
(eicosatetraenoate, C20:4 amp + ettcoa3 + ppi n-3), adipocyte
FACOAL204_6_ac fatty-acid--CoA ligase [a]: atp + coa + ecsttea6
<==> Fatty Acid Synthesis EC-6.2.1.3 (eicosatetraenoate,
C20:4 amp + ettcoa6 + ppi n-6), adipocyte FACOAL205_3_ac
fatty-acid--CoA ligase [a]: atp + coa + ecspea3 <==> Fatty
Acid Synthesis EC-6.2.1.3 (eicosapentaenoate, amp + espcoa3 + ppi
C20:5 n-3), adipocyte FACOAL220_ac fatty-acid--CoA ligase [a]: atp
+ coa + dcsa <==> amp + Fatty Acid Synthesis EC-6.2.1.3
(docosanoate, C22:0), dcsacoa + ppi adipocyte FACOAL221_11_ac
fatty-acid--CoA ligase [a]: atp + coa + dcsea11 <==>
Fatty Acid Synthesis EC-6.2.1.3 (docosenoate, C22:1 n- amp +
dsecoa11 + ppi 11), adipocyte FACOAL221_9_ac fatty-acid--CoA ligase
[a]: atp + coa + dcsea9 <==> amp + Fatty Acid Synthesis
EC-6.2.1.3 (docosenoate, C22:1 n-9), dsecoa9 + ppi adipocyte
FACOAL224_6_ac fatty-acid--CoA ligase [a]: atp + coa + ocsttea6
<==> Fatty Acid Synthesis EC-6.2.1.3 (ocosatetraenoate, C22:4
amp + osttcoa6 + ppi n-6), adipocyte FACOAL225_3_ac fatty-acid--CoA
ligase [a]: atp + coa + dcspea3 <==> Fatty Acid Synthesis
EC-6.2.1.3 (docosapentaenoate, amp + dspcoa3 + ppi C22:5 n-3),
adipocyte FACOAL225_6_ac fatty-acid--CoA ligase [a]: atp + coa +
dcspea6 <==> Fatty Acid Synthesis EC-6.2.1.3
(docosapentaenoate, amp + dspcoa6 + ppi C22:5 n-6), adipocyte
FACOAL226_6_ac fatty-acid--CoA ligase [a]: atp + coa + dcshea3
<==> Fatty Acid Synthesis EC-6.2.1.3 (docosahexaenoate, amp +
dshcoa3 + ppi C22:6 n-6), adipocyte FAS100_ac fatty acid synthase
(n- [a]: (3) h + malcoa + (2) nadph + Fatty Acid Synthesis
EC-2.3.1.85 C10:0), adipocyte octa --> co2 + coa + dca + h2o +
(2) nadp FAS120_ac fatty acid synthase (n- [a]: dca + (3) h +
malcoa + (2) Fatty Acid Synthesis EC-2.3.1.85 C12:0), adipocyte
nadph --> co2 + coa + ddca + h2o + (2) nadp FAS140_ac fatty acid
synthase (n- [a]: ddca + (3) h + malcoa + (2) Fatty Acid Synthesis
EC-2.3.1.85 C14:0), adipocyte nadph --> co2 + coa + h2o + (2)
nadp + ttdca FAS150_ac fatty acid synthase [a]: (17) h + (6) malcoa
+ (12) Fatty Acid Synthesis (C15:0), adipocyte cytosol nadph +
ppcoa --> (6) co2 + (7) coa + (5) h2o + (12) nadp + ptdca
FAS160_ac fatty acid synthase (n- [a]: (3) h + malcoa + (2) nadph +
Fatty Acid Synthesis EC-2.3.1.85 C16:0), adipocyte ttdca --> co2
+ coa + h2o + hdca + (2) nadp FAS170_ac fatty acid synthase [a]:
(3) h + malcoa + (2) nadph + Fatty Acid Synthesis (C17:0),
adipocyte cytosol ptdca --> co2 + coa + h2o + hpdca + (2) nadp
FAS180_ac fatty acid synthase (n- [a]: (3) h + hdca + malcoa + (2)
Fatty Acid Synthesis EC-2.3.1.85 C18:0), adipocyte nadph --> co2
+ coa + h2o + (2) nadp + ocdca FAS200_ac fatty acid synthase (n-
[a]: (3) h + malcoa + (2) nadph + Fatty Acid Synthesis EC-2.3.1.85
C20:0), adipocyte ocdca --> co2 + coa + ecsa + h2o + (2) nadp
FAS220_ac fatty acid synthase (n- [a]: ecsa + (3) h + malcoa + (2)
Fatty Acid Synthesis EC-2.3.1.85 C22:0), adipocyte nadph --> co2
+ coa + dcsa + h2o + (2) nadp FAS80_L_ac fatty acid synthase (n-
[a]: accoa + (8) h + (3) malcoa + Fatty Acid Synthesis EC-2.3.1.85
C8:0), lumped reaction, (6) nadph --> (3) co2 + (4) coa +
adipocyte (2) h2o + (6) nadp + octa GAT1_ac_HS_ub unbalanced
glycerol 3- [a]: (0.00032) dcsacoa + Fatty Acid Synthesis phosphate
acyltransferase (0.00698) ddcoa + (0.00024) (glycerol 3-phosphate),
dsecoa11 + (0.00056) dsecoa9 + adipocyte cytosol, Homo (0.00172)
dshcoa3 + (0.00163) sapiens specific dspcoa3 + (0.00016) dspcoa6 +
(0.00182) ecsacoa + (0.00272) esdcoa6 + (0.00035) esdcoa9 +
(0.00148) esecoa11 + (0.00026) esecoa7 + (0.00732) esecoa9 +
(0.00036) espcoa3 + (0.00027) estcoa3 + (0.0023) estcoa6 +
(0.00027) ettcoa3 + (0.00311) ettcoa6 + glyc3p + (0.02985) hdcoa7 +
(0.00582) hdcoa9 + (0.00295) hpdcoa8 + (0.15761) ocdycacoa6 +
(0.00499) odcoa3 + (0.00039) odcoa6 + (0.0026) odecoa5 + (0.01831)
odecoa7 + (0.39309) odecoa9 + (0.00138) osttcoa6 + (0.00375) pdcoa
+ (0.24351) pmtcoa + (0.06379) strcoa + (0.03728) tdcoa + (0.00244)
tdecoa5 + (0.00037) tdecoa7 --> 1ag3p_HS + coa 12DGRH_ac_HS_ub
unbalanced diacylglycerol [a]: 12dgr_HS + h2o --> (0.00032)
Triglycerol Degradation EC-3.1.1.3 hydrolase, adipocyte dcsa +
(0.00024) dcsea11 + cytosol, Homo sapiens (0.00056) dcsea9 +
(0.00172) specific dcshea3 + (0.00163) dcspea3 + (0.00016) dcspea6
+ (0.00698) ddca + (0.00182) ecsa + (0.00272) ecsdea6 + (0.00035)
ecsdea9 + (0.00148) ecsea11 + (0.00026) ecsea7 + (0.00732) ecsea9 +
(0.00036) ecspea3 + (0.00027) ecstea3 + (0.0023) ecstea6 +
(0.00027) ecsttea3 + (0.00311) ecsttea6 + h + (0.24351) hdca +
(0.02985) hdcea7 + (0.00582) hdcea9 + (0.00295) hpdcea8 + mglyc_HS
+ (0.06379) ocdca + (0.0026) ocdcea5 + (0.01831) ocdcea7 +
(0.39309) ocdcea9 + (0.00499) ocdctra3 + (0.00039) ocdctra6 +
(0.15761) ocddea6 + (0.00138) ocsttea6 + (0.00375) ptdca +
(0.03728) ttdca + (0.00244) ttdcea5 + (0.00037) ttdcea7
MGLYCH_ac_HS_ub unbalanced monoglycerol [a]: h2o + mglyc_HS -->
(0.00032) Triglycerol Degradation EC-3.1.1.3 hydrolase, adipocyte
dcsa + (0.00024) dcsea11 + cytosol, Homo sapiens (0.00056) dcsea9 +
(0.00172) specific dcshea3 + (0.00163) dcspea3 + (0.00016) dcspea6
+ (0.00698) ddca + (0.00182) ecsa + (0.00272) ecsdea6 + (0.00035)
ecsdea9 + (0.00148) ecsea11 + (0.00026) ecsea7 + (0.00732) ecsea9 +
(0.00036) ecspea3 + (0.00027) ecstea3 + (0.0023) ecstea6 +
(0.00027) ecsttea3 + (0.00311) ecsttea6 + glyc + h + (0.24351) hdca
+ (0.02985) hdcea7 + (0.00582) hdcea9 + (0.00295) hpdcea8 +
(0.06379) ocdca + (0.0026) ocdcea5 + (0.01831) ocdcea7 + (0.39309)
ocdcea9 + (0.00499) ocdctra3 + (0.00039) ocdctra6 + (0.15761)
ocddea6 + (0.00138) ocsttea6 + (0.00375) ptdca + (0.03728) ttdca +
(0.00244) ttdcea5 + (0.00037) ttdcea7 TRIGH_ac_HS_ub unbalanced
triacylglycerol [a]: h2o + triglyc_HS --> Triglycerol
Degradation EC-3.1.1.3 hydrolase, adipocyte 12dgr_HS + cytosol,
Homo sapiens (0.00032) dcsa + (0.00024) specific dcsea11 +
(0.00056) dcsea9 + (0.00172) dcshea3 + (0.00163) dcspea3 +
(0.00016) dcspea6 + (0.00698) ddca + (0.00182) ecsa + (0.00272)
ecsdea6 + (0.00035) ecsdea9 + (0.00148) ecsea11 + (0.00026) ecsea7
+ (0.00732) ecsea9 + (0.00036) ecspea3 + (0.00027) ecstea3 +
(0.0023) ecstea6 + (0.00027) ecsttea3 + (0.00311) ecsttea6 + h +
(0.24351) hdca + (0.02985) hdcea7 + (0.00582) hdcea9 + (0.00295)
hpdcea8 + (0.06379) ocdca + (0.0026) ocdcea5 + (0.01831) ocdcea7 +
(0.39309) ocdcea9 + (0.00499) ocdctra3 + (0.00039) ocdctra6 +
(0.15761) ocddea6 + (0.00138) ocsttea6 + (0.00375) ptdca +
(0.03728) ttdca + (0.00244) ttdcea5 + (0.00037) ttdcea7
DAGPYP_ac_HS_ub unbalanced diacylglycerol [a]: h2o + pa_HS -->
12dgr_HS + Triglycerol Synthesis EC-3.1.3.4 pyrophosphate pi
phosphatase, adipocyte cytosol, Homo sapiens specific
TRIGS_ac_HS_ub unbalanced triglycerol [a]: 12dgr_HS + (0.00032)
Triglycerol Synthesis synthesis, adipocyte dcsacoa + (0.00698)
ddcoa + cytosol, Homo sapiens (0.00024) dsecoa11 + (0.00056)
specific dsecoa9 + (0.00172) dshcoa3 + (0.00163) dspcoa3 +
(0.00016) dspcoa6 + (0.00182) ecsacoa + (0.00272) esdcoa6 +
(0.00035) esdcoa9 + (0.00148) esecoa11 + (0.00026) esecoa7 +
(0.00732) esecoa9 + (0.00036) espcoa3 + (0.00027) estcoa3 +
(0.0023) estcoa6 + (0.00027) ettcoa3 + (0.00311) ettcoa6 +
(0.02985) hdcoa7 + (0.00582) hdcoa9 + (0.00295) hpdcoa8 + (0.15761)
ocdycacoa6 + (0.00499) odcoa3 + (0.00039) odcoa6 + (0.0026) odecoa5
+ (0.01831) odecoa7 + (0.39309) odecoa9 + (0.00138) osttcoa6 +
(0.00375) pdcoa + (0.24351) pmtcoa + (0.06379) strcoa + (0.03728)
tdcoa + (0.00244) tdecoa5 + (0.00037) tdecoa7 --> coa +
triglyc_HS NDPK1_ac nucleoside-diphosphate [a]: atp + gdp
<==> adp + gtp Nucleotide Metabolism EC-2.7.4.6 kinase
(ATP:GDP) NDPK1_mc nucleoside-diphosphate [y]: atp + gdp <==>
adp + gtp Nucleotide Metabolism EC-2.7.4.6 kinase (ATP:GDP) ADK1_mc
adenylate kinase, myocyte [y]: amp + atp <==> (2) adp
Nucleotide Salvage EC-2.7.4.3 cytosolic Pathways NTPP6m_ac
Nucleoside triphosphate [b]: atp + h2o --> amp + h + ppi
Nucleotide Salvage pyrophosphorylase (atp), Pathways adipocyte
mitochondrial ADK1_ac adenylate kinase, [a]: amp + atp <==>
(2) adp Nucleotide Savage EC-2.7.4.3 adipocyte cytosolic Pathway
CAT_ac catalase, adipocyte [a]: (2) h2o2 --> (2) h2o + o2 Other
EC-1.11.1.6 cytosolic HCO3E_ac carbonate dehydratase [a]: co2 + h2o
<==> h + hco3 Other EC-4.2.1.1 (HCO3 equilibration reaction),
adipocyte cytosolic HCO3E_mc carbonate dehydratase [y]: co2 + h2o
<==> h + hco3 Other EC-4.2.1.1 (HCO3 equilibration reaction),
myocyte cytosolic HCO3Ei carbonate dehydratase [i]: co2 + h2o
<==> h + hco3 Other EC-4.2.1.1 (HCO3 equilibration reaction),
intra-organism NH4DIS_ac nh4 Dissociation [a]: nh4 <==> h +
nh3 Other CONTRACTION_mc muscle contraction, [y]: myoactinADPPi
--> adp + Contraction myocyte cytosol myoactin + pi MYOADPPIA_mc
myosin-ADP-Pi [y]: actin + myosinADPPi --> Contraction
attachment, myocyte myoactinADPPi cytosol MYOSINATPB_mc mysosin ATP
binding, [y]: atp + myoactin --> actin + Contraction myocyte
cytosol myosinATP MYOSINATPH_mc myosin-ATP hydrolysis, [y]: h2o +
myosinATP --> h + Contraction myocyte cytosol myosinADPPi
CREATt2is_mc Creatine Na+ symporter, creat[i] + na1[c] <==>
creat[y] + Transport myocyte cytosol na1[y] CRTNtis_mc creatinine
transport, crtn[i] <==> crtn[y] Transport myocyte cytosol
Clt_xo chlorideion transport out cl[e] --> cl[i] Transport via
diffusion DCSAtis_ac docosanoate (C22:0) dcsa[a] --> dcsa[i]
Transport adipocyte transport DCSEA11tis_ac docosenoate (C22:1,
n-11) dcsea11[a] --> dcsea11[i]
Transport adipocyte transport DCSEA9tis_ac docosenoate (C22:1, n-9)
dcsea9[a] --> dcsea9[i] Transport adipocyte transport DCSHEA3t
docosahexaenoate dcshea3[e] <==> dcshea3[i] Transport (C22:6,
n-3) transport DCSHEA3tis_ac docosahexaenoate dcshea3[i] <==>
dcshea3[a] Transport (C22:6, n-3) adipocyte transport DCSPEA3t
Docosapentaenoate dcspea3[e] <==> dcspea3[i] Transport
(C22:5, n-3) transport DCSPEA3tis_ac Docosapentaenoate dcspea3[i]
<==> dcspea3[a] Transport (C22:5, n-3) adipocyte transport
DCSPEA6t Docosapentaenoate dcspea6[e] <==> dcspea6[i]
Transport (C22:5, n-6) transport DCSPEA6tis_ac Docosapentaenoate
dcspea6[i] <==> dcspea6[a] Transport (C22:5, n-6) adipocyte
transport DDCAtis_ac dodecanoate (C12:0) ddca[a] --> ddca[i]
Transport adipocyte transport DDCAtis_mc dodecanoate (C12:0)
ddca[i] --> ddca[y] Transport myocyte transport ECSAtis_ac
eicosanoate (C20:0) ecsa[a] --> ecsa[i] Transport adipocyte
transport ECSDEA6t Eicosadienoate (C20:2, n- ecsdea6[e] <==>
ecsdea6[i] Transport 6) transport ECSDEA6tis_ac Eicosadienoate
(C20:2, n- ecsdea6[i] <==> ecsdea6[a] Transport 6) adipocyte
transport ECSDEA9tis_ac eicosadienoate (C20:2, n- ecsdea9[a] -->
ecsdea9[i] Transport 9) adipocyte transport ECSEA11tis_ac
eicosenoate (C20:1, n-11) ecsea11[a] --> ecsea11[i] Transport
adipocyte transport ECSEA7tis_ac eicosenoate (C20:1, n-7) ecsea7[a]
--> ecsea7[i] Transport adipocyte transport ECSEA9tis_ac
eicosenoate (C20:1, n-9) ecsea9[a] --> ecsea9[i] Transport
adipocyte transport ECSFAtis_mc eicosanoate transport (n- ecsa[i]
<==> ecsa[y] Transport C20:0) ECSPEA3t Eicosapentaenoate
ecspea3[e] <==> ecspea3[i] Transport (C20:5, n-3) transport
ECSPEA3tis_ac Eicosapentaenoate ecspea3[i] <==> ecspea3[a]
Transport (C20:5, n-3) adipocyte transport ECSTEA3t Eicosatrienoate
(C20:3, n- ecstea3[e] <==> ecstea3[i] Transport 3) transport
ECSTEA3tis_ac Eicosatrienoate (C20:3, n- ecstea3[i] <==>
ecstea3[a] Transport 3) adipocyte transport ECSTEA6t
Eicosatrienoate (C20:3, n- ecstea6[e] <==> ecstea6[i]
Transport 6) transport ECSTEA6tis_ac Eicosatrienoate (C20:3, n-
ecstea6[i] <==> ecstea6[a] Transport 6) adipocyte transport
ECSTTEA3t Eicosatetraenoate (C20:4, ecsttea3[e] <==>
ecsttea3[i] Transport n-3) transport ECSTTEA3tis_ac
Eicosatetraenoate (C20:4, ecsttea3[i] <==> ecsttea3[a]
Transport n-3) adipocyte transport ECSTTEA6t Eicosatetraenoate
(C20:4, ecsttea6[e] <==> ecsttea6[i] Transport n-6) transport
ECSTTEA6tis_ac Eicosatetraenoate (C20:4, ecsttea6[i] <==>
ecsttea6[a] Transport n-6) adipocyte transport GLYCt6is_ac glycerol
transport in/out glyc[a] + h[a] <==> glyc[i] + h[i] Transport
via symporter, adipocyte HCO3t2 HCO3 transport out via hco3[e]
<==> hco3[i] Transport diffusion HDCAtis_ac hexadecanoate
(C16:0) hdca[a] --> hdca[i] Transport adipocyte transport
HDCAtis_mc hexadecanoate (C16:0) hdca[i] --> hdca[y] Transport
myocyte transport HDCEA7tis_ac hexadecenoate (C16:1, n- hdcea7[a]
--> hdcea7[i] Transport 7) adipocyte transport HDCEA9tis_ac
hexadecenoate (C16:1, n- hdcea9[a] --> hdcea9[i] Transport 9)
adipocyte transport HPDCEA8tis_ac heptadecenoate (C17:1, n-
hpdcea8[a] --> hpdcea8[i] Transport 8) adipocyte transport
ILEtis_ac L-isoeucine transport h[i] + ile-L[i] <==> h[a] +
ile-L[a] Transport TC-2.A.26 in/out via proton symport, adipocyte
NAt sodium transport in/out via h[i] + na1[e] <==> h[e] +
na1[i] Transport TC-2.A.36 proton antiport (one H+) NAtis_mc sodium
transport in/out via na1[i] <==> na1[y] Transport TC-1.A.15
the non-selective cation channel NH4CLt_xo ammonium chloride cl[i]
+ nh4[i] <==> cl[e] + nh4[e] Transport transport NH4tis_ac
ammonia transport via nh4[i] <==> nh4[a] Transport diffusion,
adipocyte cytosolic OCDCAtis_ac octadecanoate (C18:0) ocdca[a]
--> ocdca[i] Transport adipocyte transport OCDCAtis_mc
octadecanoate (C18:0) ocdca[i] --> ocdca[y] Transport myocyte
transport OCDCEA5tis_ac octadecenoate (C18:1, n- ocdcea5[a] -->
ocdcea5[i] Transport 5) adipocyte transport OCDCEA7tis_ac
octadecenoate (C18:1, n- ocdcea7[a] --> ocdcea7[i] Transport 7)
adipocyte transport OCDCEA9tis_ac octadecenoate (C18:1, n-
ocdcea9[a] --> ocdcea9[i] Transport 9) adipocyte transport
OCDCEA9tis_mc octadecenoate (C18:1, n- ocdcea9[i] --> ocdcea9[y]
Transport 9) myocyte transport OCDCTRA3t Octadecatrienoate (C18:3,
ocdctra3[e] <==> ocdctra3[i] Transport n-3) transport
OCDCTRA3tis_ac Octadecatrienoate (C18:3, ocdctra3[i] <==>
ocdctra3[a] Transport n-3) adipocyte transport OCDCTRA6t
Octadecatrienoate (C18:3, ocdctra6[e] <==> ocdctra6[i]
Transport n-6) transport OCDCTRA6tis_ac Octadecatrienoate (C18:3,
ocdctra6[i] <==> ocdctra6[a] Transport n-6) adipocyte
transport OCDDEA6t Octadecadienoate (C18:2, ocddea6[e] <==>
ocddea6[i] Transport n-6) transport OCDDEA6tis_ac Octadecadienoate
(C18:2, ocddea6[i] <==> ocddea6[a] Transport n-6) adipocyte
transport OCSTTEA6t Ocosatetraenoate (C22:4, ocsttea6[e] <==>
ocsttea6[i] Transport n-6) transport OCSTTEA6tis_ac
Ocosatetraenoate (C22:4, ocsttea6[i] <==> ocsttea6[a]
Transport n-6) adipocyte transport PIt2_xo phosphate transport in
via h[e] + pi[e] <==> h[i] + pi[i] Transport proton symport
PTDCAtis_ac pentadecanoate (C15:0) ptdca[a] --> ptdca[i]
Transport adipocyte transport PTDCAtis_mc pentadecanoate (C15:0)
ptdca[i] --> ptdca[y] Transport myocyte transport TTDCAtis_ac
tetradecanoate (C14:0) ttdca[a] --> ttdca[i] Transport adipocyte
transport TTDCAtis_mc tetradecanoate (C14:0) ttdca[i] -->
ttdcat[y] Transport myocyte transport TTDCEA5tis_ac tetradecenoate
(C14:1, n- ttdcea5[a] --> ttdcea5[i] Transport 5) adipocyte
transport TTDCEA7tis_ac tetradecenoate (C14:1, n- ttdcea7[a] -->
ttdcea7[i] Transport 7) adipocyte transport G6Pter_ac glucose
6-phosphate g6p[a] <==> g6p[f] Transport, adipocyte
endoplasmic Endoplasmic Reticular reticular transport via diffusion
G6Pter_mc glucose 6-phosphate g6p[y] <==> g6p[u] Transport,
myocyte endoplasmic Endoplasmic Reticular reticular transport via
diffusion GLCter_ac glucose transport, glc-D[a] <==> glc-D[f]
Transport, endoplasmic reticulum Endoplasmic Reticular GLCter_mc
glucose transport, glc-D[y] <==> glc-D[u] Transport,
endoplasmic reticulum Endoplasmic Reticular CO2t_xo CO2 transport
via diffusion co2[e] <==> co2[i] Transport, Extracellular
CO2tis_ac CO2 adipocyte transport co2[i] <==> co2[a]
Transport, Extracellular out via diffusion CO2tis_mc CO2 myocyte
transport co2[i] <==> co2[y] Transport, Extracellular out via
diffusion CRTNt creatinine transport crtn[i] <==> crtn[e]
Transport, Extracellular GLCt1_xo glucose transport (uniport:
glc-D[e] <==> glc-D[i] Transport, Extracellular facilitated
diffusion), intra- organism GLCt1is_ac glucose transport into
glc-D[i] <==> glc-D[a] Transport, Extracellular adipocyte
(uniport: facilitated diffusion) GLCt1is_mc glucose transport into
glc-D[i] <==> glc-D[y] Transport, Extracellular myocyte
(uniport: facilitated diffusion) H2Ot5_xo H2O transport via
diffusion h2o[e] <==> h2o[i] Transport, Extracellular
H2Ot5is_ac H2O transport into h2o[i] <==> h2o[a] Transport,
Extracellular adipocyte via diffusion H2Ot5is_mc H2O transport into
h2o[i] <==> h2o[y] Transport, Extracellular myocyte via
diffusion ILEt L-isoeucine transport h[e] + ile-L[e] <==>
h[i] + ile-L[i] Transport, Extracellular TC-2.A.26 in/out via
proton symport L-LACt2_xo L-lactate transport via h[e] + lac-L[e]
<==> h[i] + lac-L[i] Transport, Extracellular proton symport
L-LACt2is_mc L-lactate reversible h[i] + lac-L[i] <==> h[y] +
lac-L[y] Transport, Extracellular transport into myocyte via proton
symport O2t_xo O2 transport via diffusion o2[e] <==> o2[i]
Transport, Extracellular O2tis_ac O2 transport into o2[i]
<==> o2[a] Transport, Extracellular adipocyte via diffusion
O2tis_mc O2 transport into myocyte o2[i] <==> o2[y]
Transport, Extracellular via diffusion Plt2_xo [deleted phosphate
transport in via h[e] + pi[e] --> h[i] + pi[i] Transport,
Extracellular Aug. 26, 2004 proton symport 01:34:57 PM] Plt6is_ac
phosphate transport in/out h[i] + pi[i] <==> h[a] + pi[a]
Transport, Extracellular TC-2.A.20 of adipocyte via proton
symporter PIt6is_mc phosphate transport in/out h[i] + pi[i]
<==> h[y] + pi[y] Transport, Extracellular TC-2.A.20 of
myocyte via proton symporter 3MOPtm_ac 3-Methyl-2-oxopentanoate
3mop[a] <==> 3mop[b] Transport, transport, diffusion,
Mitochondrial adipocyte mitochondrial ATP/ADPtm_ac ATP/ADP
transport, adp[a] + atp[b] <==> adp[b] + Transport, adipocyte
mitochondrial atp[a] Mitochondrial ATP/ADPtm_mc ATP/ADP transport,
adp[y] + atp[z] <==> adp[z] + atp[y] Transport,
myocyte mitochondrial Mitochondrial CITtam_ac citrate transport,
adipocyte cit[a] + mal-L[b] <==> cit[b] + mal- Transport,
mitochondrial L[a] Mitochondrial CITtam_mc citrate transport,
myocyte cit[y] + mal-L[z] <==> cit[z] + mal- Transport,
mitochondrial L[y] Mitochondrial CO2tm_ac CO2 transport
(diffusion), co2[a] <==> co2[b] Transport, adipocyte
mitochondrial Mitochondrial CO2tm_mc CO2 transport (diffusion),
co2[y] <==> co2[z] Transport, myocyte mitochondrial
Mitochondrial CRNCARtm_mc carnithine-acetylcarnithine acrn[y] +
crn[z] --> acrn[z] + crn[y] Transport, carrier, myocyte
Mitochondrial mitochondrial CRNODETm_mc carnitine 9-cis- coa[z] +
odecrn9[y] <==> crn[y] + Transport, octadecenoyltransferase
odecoa9[z] Mitochondrial II, myocyte CRNPTDTm_mc carnitine coa[z] +
pdcrn[y] <==> crn[y] + Transport, pentadecanoyltransferase
pdcoa[z] Mitochondrial II, myocyte DHAP1tm_ac dihydroxyacetone
dhap[a] <==> dhap[b] Transport, phosphate transport,
Mitochondrial adipocyte mitochondrial DHAP1tm_mc dihydroxyacetone
dhap[y] <==> dhap[z] Transport, phosphate transport,
Mitochondrial myocyte mitochondrial GACm_ac glutamate aspartate
asp-L[b] + glu-L[a] + h[a] --> asp- Transport, carrier,
adipocyte L[a] + glu-L[b] + h[b] Mitochondrial
cytosolic/mitochondrial GACm_mc glutamate aspartate asp-L[z] +
glu-L[y] + h[y] --> asp- Transport, carrier, myocyte L[y] +
glu-L[z] + h[z] Mitochondrial cytosolic/mitochondrial GL3Ptm_mc
glycerol-3-phosphate glyc3p[y] <==> glyc3p[z] Transport,
transport, myocyte Mitochondrial mitochondrial GTPt3m_ac GTP/GDP
transporter, gdp[b] + gtp[a] + h[a] --> gdp[a] + Transport,
adipocyte mitochondrial gtp[b] + h[b] Mitochondrial GTPt3m_mc
GTP/GDP transporter, gdp[z] + gtp[y] + h[y] --> gdp[y] +
Transport, myocyte mitochondrial gtp[z] + h[z] Mitochondrial
H2Otm_ac H2O transport, adipocyte h2o[a] <==> h2o[b]
Transport, mitochondrial Mitochondrial H2Otm_mc H2O transport,
myocyte h2o[y] <==> h2o[z] Transport, mitochondrial
Mitochondrial MALAKGtm_ac malate-alphaketoglutarate akg[b] +
mal-L[a] --> akg[a] + mal- Transport, transporter, adipocyte
L[b] Mitochondrial mitochondria MALAKGtm_mc
malate-alphaketoglutarate akg[z] + mal-L[y] --> akg[y] + mal-
Transport, transporter, myocyte L[z] Mitochondrial mitochondria
O2trm_ac O2 transport into o2[a] <==> o2[b] Transport,
adipocyte mitochondria Mitochondrial (diffusion) O2trm_mc O2
transport into myocyte o2[y] <==> o2[z] Transport,
mitochondria (diffusion) Mitochondrial Pltm_ac phosphate
transporter, h[a] + pi[a] <==> h[b] + pi[b] Transport,
adipocyte mitochondrial Mitochondrial Pltm_mc phosphate
transporter, h[y] + pi[y] <==> h[z] + pi[z] Transport,
myocyte mitochondrial Mitochondrial PPAtm_ac propionate transport
in/out h[a] + ppa[a] <==> h[b] + ppa[b] Transport, TC-2.A.20
via proton symport, Mitochondrial adipocyte PYRtm_ac pyruvate
transport, h[a] + pyr[a] <==> h[b] + pyr[b] Transport,
adipocyte mitochondrial Mitochondrial PYRtm_mc pyruvate transport,
h[y] + pyr[y] <==> h[z] + pyr[z] Transport, myocyte
mitochondrial Mitochondrial CRNCARtp_mc carnithine-acetylcarnithine
acrn[y] + crn[w] <==> acrn[w] + Transport, Peroxisomal
carrier, myocyte crn[y] peroxixome
* * * * *