JP2010146578A

Human metabolic model and method

Abstract

Problem to be solved.To provide an in silico model for determining the physiological function of human cells including human skeletal muscle cells.

Solution.The models include a data structure relating a plurality of Homo sapiens reactions, a constraint set for the plurality of Homo sapiens reactions, and commands for determining a distribution of flux through the reactions that is predictive of a Homo sapiens physiological function. The model can further include a gene database containing information characterizing the associated gene or genes. A regulated Homo sapiens reaction can be represented in the model by including a variable constraint for the regulated reaction. The invention further provides methods for making an in silico Homo sapiens physiological function using the model.

JP2010146578A, drawing sheet 1 — Sheet 1 of 37

Term

Projected expiry 28 December 2029.

1 claim: 1 independent, 0 dependent

1
A program stored on a computer-readable medium for performing the methods described herein. コンピューター読み取り可能媒体上に格納されたプログラムであって、本願明細書に記載の方法を実行するためのプログラム。

114 paragraphs, as filed

(Background of invention) The present invention generally relates to the analysis of the activity of the chemical reaction network, and more specifically to the calculation method for simulating and predicting the activity of the Homo sapiens reaction network.

Therapeutic agents (including drugs and gene-based agents) are being rapidly developed by the pharmaceutical industry with the goal of preventing or treating human diseases. Dietary supplements (including herbal products, vitamins and amino acids) have also been developed and marketed in the dietary supplement industry. Due to the complexity of the biochemical reaction network within and between human cells, it is relatively important to be caused by therapeutic agents or large amounts of food components in the abundance or activity of a particular target (eg, biotransforms, genes or proteins). Even non-perturbation can affect hundreds of biochemical reactions. These upsets are desired It can lead to therapeutic effects (eg, cell quiescence or cell death in the case of cancer cells or other pathogenic hyperproliferative cells). However, these agitation can also lead to unwanted side effects (eg, production of toxic by-products) if the systemic effects of the agitation are not taken into account.

Current approaches to drug development and dietary supplement development do not take into account the effects of agitation on molecular targets on systemic cellular behavior. Understanding human cell behavior from an integrated perspective is essential in order to design effective methods for restoring, manipulating or incapacitating cell activity.

Cell metabolism, which is an example of a process involving a highly integrated network of biochemical reactions, is essential for all normal cellular processes or all physiological processes. These processes include: homeostasis, proliferation, differentiation, programmed cell death (apoptosis), and motility. Modifications in cell metabolism characterize a vast number of human diseases. For example, tissue damage is often characterized by increased catabolism of glucose, fatty acids and amino acids, which, if persistent, can lead to organ dysfunction. Hypoxia and hyponutrition conditions, such as those that occur in solid tumors, result in a myriad of adaptive metabolic changes, including activation of glycolysis and neovascularization. Metabolic dysfunction also contributes to neurodegenerative diseases, heart disease, neuromuscular diseases, obesity and diabetes. Currently, despite the importance of cell metabolism to normal and pathological processes, a detailed systemic understanding of cell metabolism in human cells is lacking.

Therefore, there is a need for a model to describe the Homo sapiens response network (core metabolic response network and metabolic response network in specific cell types), which differs in human cell behavior under physiological, pathological and therapeutic conditions. It can be used to simulate the situation. The present invention meets this need and also provides related advantages.

(Gist of the invention) The present invention provides computer-readable media including: (a) a data structure that associates multiple Homo sapiens reactions with multiple Homo sapiens reactants, wherein each of the above Homo sapiens reactions is: Reactants identified as substrates for the reaction, reactants identified as products of the reaction, and data structures containing the substrates and stoichiometric coefficients associated with the products, (b) the plurality of Homo sapiens reactions. A constraint set, and (c) a command to determine at least one flow flux distribution, which minimizes or maximizes the objective function when the constraint set is applied to the data representation, where A command in which at least one of the above flow flux distributions can predict Homo sapiens physiological function. In one embodiment, the Homo in the data structure At least one of the sapiens reactions is annotated to indicate the relevant gene, and the computer-readable medium further comprises a gene database containing information that characterizes the relevant gene. In another embodiment, at least one of the Homo sapiens reactions is a regulatory reaction, and the computer-readable medium further comprises a binding set for the plurality of Homo sapiens reactions, which is said to be the above. Includes variable constraints on regulatory responses. The present invention provides a method for predicting Homo sapiens physiological function, the method comprising: (a) providing a data structure for associating multiple Homo sapiens reactions with multiple Homo sapiens reactants. Here, each of the above-mentioned Homo sapiens reactions is a reaction product identified as a substrate of the above-mentioned reaction, a reaction product identified as a product of the above-mentioned reaction, and a chemical amount related to the above-mentioned substrate and the above-mentioned product. Including the theory coefficient; (b) providing a binding set for the multiple Homo sapiens reactions described above; (c) providing an objective function; and (d) determining at least one flow flux distribution. The process of minimizing or maximizing the objective function, thereby predicting Homo sapiens physiological function, when the binding set is applied to the data representation. In one embodiment, at least one of the Homo sapiens reactions in the data structure is annotated to indicate a related gene, and the method predicts Homo sapiens physiological function associated with the gene. The present invention provides a method of predicting Homo sapiens physiological function, the method comprising: (a) providing a data structure that associates multiple Homo sapiens reactions with multiple Homo sapiens reactants. Here, each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction, a reactant identified as a product of the reaction, and a stoichiometry associated with the substrate and the product. Including the stoichiometric coefficient, where at least one of the Homo sapiens reactions is a regulatory reaction; (b) a step of providing a binding set for the plurality of Homo sapiens reactions, the binding set. Includes a variable constraint on the regulatory response; (c) a step of providing a condition-dependent value for the variable constraint; (d) a step of providing an objective function, and (e) at least one. The step of determining the flow flux distribution, which minimizes or maximizes the objective function when the constraint set is applied to the data representation, thereby predicting Homo sapiens physiological function. The invention also provides a method of creating a data structure that associates multiple Homo sapiens reactions with multiple Homo sapiens reactants in a computer readable medium, the method comprising: (a). Multiple Homo sapiens Reactions and Steps to Identify Multiple Homo sapiens Reactants as Substrate and Products of the Homo sapiens Reactants; (b) In the data structure, the Multiple Homo sapiens Reactants are referred to as the Multiple Homo sapiens Reactions. a step of associating a, wherein each of the Homo sapiens reaction, the reaction product was identified as a substrate of the reaction, the reaction product was identified as the product of the reaction, and the substrate contact associated with and the product Steps, including stoichiometric coefficients; (c) Multiple Homo above The step of determining the binding set for the sapiens reaction; (d) the step of providing the objective function; (e) the step of determining at least one flow flux distribution, where the binding set applies to the data representation. To minimize or maximize the objective function, and (f) if at least one of the above flux distributions is unpredictable in Homo sapiens physiological function, add a reaction from the above data structures or A step of deleting the reaction, repeating step (e), and storing the data structure on a computer readable medium if at least one of the above flux distributions is predictable of Homo sapiens physiological function. The present invention further provides a data structure that associates a plurality of Homo sapiens reactants with a plurality of Homo sapiens reactions, wherein the data structure is made by the method.(Item 1)Computer-readable media, including: (a) A data structure that associates a plurality of Homo sapiens reactions with a plurality of Homo sapiens reactants, wherein each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction, as a product of the reaction. A data structure comprising the identified reactants, as well as stoichiometric coefficients representing the substrate and the product, wherein at least one of the Homo sapiens reactions is annotated to indicate the relevant gene; (b) A gene database containing information that characterizes the relevant gene; (c) A set of constraints on the multiple Homo sapiens reactions, and (d) A command to determine at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data representation, where the at least one Flux distribution can predict Homo sapiens physiological function, command,Including the medium.(Item 2)The computer-readable medium of item 1, wherein the plurality of Homo sapiens reactions comprises at least one reaction from a peripheral metabolic pathway.(Item 3)The peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis process, amino acid decomposition process, purine biosynthesis process, pyrimidine biosynthesis process, lipid biosynthesis process, fatty acid metabolism process, cofactor biosynthesis process and cofactor transport process. The computer-readable medium described in item 2.(Item 4)The above Homo sapiens physiological functions include proliferation, energy production, redox equivalent production, biomass production, biomass precursor production, protein production, amino acid production, purine production, pyrimidine production, lipid production, and fatty acid production. The computer-readable medium of item 1, selected from the group consisting of production, production of cofactors, transport of metabolites, and consumption of carbon, nitrogen, sulfur, phosphoric acid, hydrogen and oxygen.(Item 5)The computer according to item 1, wherein the Homo sapiens physiological function is selected from the group consisting of proteolysis, amino acid degradation, purine degradation, pyrimidine degradation, lipid degradation, fatty acid degradation and cofactor degradation. Readable medium.(Item 6)The computer-readable medium according to item 1, wherein the data structure comprises a set of linear algebraic equations.(Item 7)The computer-readable medium according to item 1, wherein the data structure comprises a matrix.(Item 8)The computer readable medium according to item 1 in which the above command contains an optimization problem.(Item 9)The computer-readable medium according to item 1, wherein the above command contains a linear program.(Item 10)The computer read according to item 1, wherein at least one of the plurality of Homo sapiens reactants or at least one of the plurality of Homo sapiens reactions is annotated with an assignment to a subsystem or compartment. Possible medium.(Item 11)The first substrate or first product in the plurality of Homo sapiens reactions is substituted into the first compartment, and the second substrate or second product in the plurality of Homo sapiens reactions is substituted into the second compartment. The computer-readable medium according to item 10.(Item 12)The computer-readable medium of item 1, wherein the plurality of Homo sapiens reactions are annotated to indicate a plurality of related genes, wherein the gene database comprises information characterizing the plurality of related genes.(Item 13)Computer-readable media, including: (a) A data structure that associates a plurality of Homo sapiens reactions with a plurality of Homo sapiens reactants, wherein each of the Homo sapiens reactions is a reactant identified as a substrate of the reaction, as a product of the reaction. The data structure, which comprises the identified reactants, as well as the stoichiometric coefficients associated with the substrate and the product, wherein at least one of the Homo sapiens reactions is a regulatory reaction; (b) A binding set for the plurality of Homo sapiens reactions, wherein the binding set comprises a variable binding for the regulatory response; and (c) A command to determine at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data representation, where the at least one Flux distribution can predict Homo sapiens physiological function, command,Including the medium.(Item 14)13. The computer-readable medium of item 13, wherein the variable binding depends on the outcome of at least one reaction of the data structures.(Item 15)13. The computer-readable medium of item 13, wherein the variable binding depends on the outcome of the regulatory phenomenon.(Item 16)The computer-readable medium according to item 13, wherein the variable binding is time dependent.(Item 17)The computer-readable medium of item 13, wherein the variable binding depends on the presence of biochemical reaction network related material.(Item 18)The computer-readable medium of item 17, wherein the biochemical reaction network related product is selected from the group consisting of substrates, products, reactions, proteins, macromolecules, enzymes and genes.(Item 19)13. The computer-readable medium of item 13, wherein the plurality of reactions are regulatory reactions and the constraints of the regulatory reactions include variable constraints.(Item 20)Computer-readable media, including: (a) A data structure that associates a plurality of Homo sapiens reactants with a plurality of Homo sapiens reactions, wherein each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction, as a product of the reaction. A data structure containing the identified reactants, as well as the stoichiometric coefficients associated with the substrate and the product; (b) A set of constraints on the multiple Homo sapiens reactions; and (c) A command to determine at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data representation, where the at least one Flux distribution can predict Homo sapiens physiological function, command,Including the medium.(Item 21)Homo sapiens A method of predicting physiological function, the method of which is as follows: (a) A step of providing a data structure for associating a plurality of Homo sapiens reactions with a plurality of Homo sapiens reactants, wherein each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction. A step of comprising the reactants identified as the product of the reaction, as well as the substrate and the stoichiometric coefficients associated with the product, wherein at least one of the Homo sapiens reactions is annotated to indicate the relevant gene. ; (b) The step of providing a binding set for the plurality of Homo sapiens reactions; (c) The process of providing the objective function; and (d) The step of determining at least one flux distribution, which, when the binding set is applied to the data representation, thereby predicts the Homo sapiens physiological function for the gene.Including, methods.(Item 22)21. The method of item 21, wherein the plurality of Homo sapiens reactions comprises at least one reaction from a peripheral metabolic pathway.(Item 23)The peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis process, amino acid decomposition process, purine biosynthesis process, pyrimidine biosynthesis process, lipid biosynthesis process, fatty acid metabolism process, cofactor biosynthesis process and cofactor transport process. The method according to item 22.(Item 24)The above Homo sapiens physiological functions include proliferation, energy production, redox equivalent production, biomass production, biomass precursor production, protein production, amino acid production, purine production, pyrimidine production, lipid production, and fatty acid production. 21. The method of item 21, selected from the group consisting of production, production of cofactors, transport of metabolites, and consumption of carbon, nitrogen, sulfur, phosphoric acid, hydrogen and oxygen.(Item 25)The above Homo sapiens physiological functions include glycolysis, TCA circuit, pentose phosphate pathway, respiratory action, amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, and co. 28. The method of item 21, selected from the group consisting of factor biosynthesis, metabolite transport and carbon source, nitrogen source, oxygen source, phosphoric acid source, hydrogen source or metabolism of sulfur source. ..(Item 26)21. The method of item 21, wherein the data structure comprises a set of linear algebraic equations.(Item 27)21. The method of item 21, wherein the data structure comprises a matrix.(Item 28)21. The method of item 21, wherein the flux distribution is determined by a linear program.(Item 29)The method according to item 21, wherein the method is as follows: (e) A step of providing a modified data structure, wherein the modified data structure comprises at least one additional reaction as compared to the data structure of step (a). (f) A method of determining at least one flux distribution, which minimizes or maximizes the objective function when the constraint set is applied to the modified data structure, thereby minimizing or maximizing the objective function. Homo sapiens Predicting Physiological Function, Process,Including, the method.(Item 30)29. The method of item 29, further comprising identifying at least one association in the at least one additional reaction.(Item 31)30. The method of item 30, wherein the step of identifying at least one association comprises associating the Homo sapiens protein with the at least one reaction.(Item 32)31. The method of item 31, further comprising identifying at least one gene encoding the protein.(Item 33)30. The method of item 30, further comprising identifying at least one compound that alters the activity or amount of at least one of the above associations, thereby identifying a candidate drug or candidate drug that alters Homo sapiens physiological function. ..(Item 34)The method according to item 21, wherein the method is as follows: (e) A step of providing a modified data structure, wherein the modified data structure lacks at least one reaction as compared to the above data structure of step (a), and (f) A method of determining at least one flux distribution, which minimizes or maximizes the objective function when the constraint set is applied to the modified data structure, thereby minimizing or maximizing the objective function. Homo sapiens Predicting Physiological Function, Process,A method that further embraces.(Item 35)34. The method of item 34, further comprising the step of identifying at least one association in the at least one reaction.(Item 36)35. The method of item 35, wherein the step of identifying at least one association comprises associating the Homo sapiens protein with the at least one reaction.(Item 37)36. The method of item 36, further comprising identifying at least one gene encoding a protein that performs at least one reaction.(Item 38)35. The method of item 35, further comprising identifying at least one compound that alters the activity or amount of at least one of the associations, thereby identifying a candidate drug or candidate drug that alters Homo sapiens physiological function. ..(Item 39)The method according to item 21, wherein the method is as follows: (e) A step of providing a modified binding set, wherein the modified binding set comprises at least one altered binding as compared to the binding for at least one reaction in the data structure of step (a). Process, and (f) A method of determining at least one flux distribution, which minimizes or maximizes the objective function when the modified constraint set is applied to the data structure, thereby minimizing or maximizing the objective function. Homo sapiens Predicting Physiological Function, Process,A method that further embraces.(Item 40)39. The method of item 39, further comprising the step of identifying at least one association in the at least one reaction.(Item 41)40. The method of item 40, wherein the step of identifying at least one association comprises associating the Homo sapiens protein with the at least one reaction.(Item 42)41. The method of item 41, further comprising identifying at least one gene encoding the protein that performs the at least one reaction.(Item 43)40. The method of item 40, further comprising identifying at least one compound that alters the activity or amount of at least one of the associations, thereby identifying a candidate drug or candidate drug that alters Homo sapiens physiological function. ..(Item 44)21. The method of item 21, further comprising providing a gene database in Homo sapiens that associates one or more reactions in the above data structures with one or more genes or one or more proteins.(Item 45)Homo sapiens A method of predicting physiological function, the method of which is as follows: (a) A step of providing a data structure for associating a plurality of Homo sapiens reactants with a plurality of Homo sapiens reactions, wherein each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction. It comprises the reactants identified as the product of the reaction, as well as the substrate and the stoichiometric coefficients associated with the product, wherein at least one of the Homo sapiens reactions is a regulatory reaction, step; (b) A step of providing a binding set for the plurality of Homo sapiens reactions, wherein the binding set comprises a variable binding for the regulatory response; (c) A step of providing a condition-dependent value for the variable binding; (d) The process of providing the objective function, and (e) The step of determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data representation, thereby Homo sapiens physiological function. Predict, process,Including, methods.(Item 46)The method of item 45, wherein the values provided for the variable binding vary depending on the outcome of at least one reaction in the data structure.(Item 47)The method of item 45, wherein the value provided for the variable binding changes depending on the outcome of the regulatory phenomenon.(Item 48)45. The method of item 45, wherein the values provided for the variable binding change over time.(Item 49)45. The method of item 45, wherein the values provided for the variable binding vary in response to the presence of biochemical reaction network associations.(Item 50)49. The method of item 49, wherein the relevant product is selected from the group consisting of substrates, products, reactions, proteins, macromolecules, enzymes and genes.(Item 51)The method of item 45, wherein the plurality of said reactions are regulatory reactions and the binding of the regulatory reaction comprises variable binding.(Item 52)A method of predicting Homo sapiens proliferative function, which is described below: (a) A step of providing a data structure for associating a plurality of Homo sapiens skeletal muscle cell reactants with a plurality of Homo sapiens skeletal muscle cell reactions, wherein each of the Homo sapiens reactions is identified as a substrate for the reaction. A step comprising the reactants, the reactants identified as the product of the reaction, and the stoichiometric coefficient associated with the substrate and the product; (b) The step of providing a binding set for the plurality of Homo sapiens reactions; (c) The process of providing the objective function, and (d) The step of determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data representation, thereby Homo sapiens physiological function. Predict, process,Including, methods.(Item 53)A method of generating a data structure that associates multiple Homo sapiens reactants with multiple Homo sapiens reactions in a computer-readable medium, the method of which is: (a) Multiple Homo sapiens reactions and the step of identifying multiple Homo sapiens reactants that are substrates and products of the Homo sapiens reactants; (b) In the data structure, a step of associating the plurality of Homo sapiens reactants with the plurality of Homo sapiens reactions, wherein each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction. A step comprising, a reactant identified as a product of the reaction, and a stoichiometric coefficient associated with the substrate and the product; (c) The step of determining the binding set for the plurality of Homo sapiens reactions; (d) The process of providing the objective function; (e) A step of determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data representation, and (f) If the at least one flow flux distribution is unpredictable in Homo sapiens physiological function, add or remove the reaction from the data structure, repeat step (e) and repeat the at least one flow. If the bundle distribution is predictable of Homo sapiens physiological function, the process of storing the data structure on a computer-readable medium,Including, the method.(Item 54)53. The method of item 53, wherein the data structure is identified from the annotated genome.(Item 55)54. The method of item 54, further comprising the step of preserving the above reactions identified from an annotated genome in a genetic database.(Item 56)53. The method of item 53, further comprising the step of annotating the reaction in the data structure.(Item 57)58. The method of item 56, wherein the annotation is selected from the group consisting of gene substitution, protein substitution, subsystem substitution, confidence grade substitution, reference to genomic annotation information, and reference to publications.(Item 58)Item 53, wherein step (b) further comprises identifying a non-equilibrium reaction in the data structure, and adding the reaction to the data structure, thereby transforming the non-equilibrium reaction into an equilibrium reaction. The method described.(Item 59)The step of adding the above reactions includes adding a reaction selected from the group consisting of in-system reactions, exchange reactions, reactions from peripheral metabolic pathways, reactions from core metabolic pathways, gene-related reactions and non-gene-related reactions. The method described in item 53.(Item 60)The reaction from the peripheral metabolic pathway consists of an amino acid biosynthesis process, an amino acid decomposition process, a purine biosynthesis process, a pyrimidine biosynthesis process, a lipid biosynthesis process, a fatty acid metabolism process, a cofactor biosynthesis process and a cofactor transport process. The method according to item 59, which is selected from.(Item 61)The above Homo sapiens physiological functions include proliferation, energy production, redox equivalent production, biomass production, biomass precursor production, protein production, amino acid production, purine production, pyrimidine production, lipid production, and fatty acid production. 53. The method of item 53, selected from the group consisting of production, production of cofactors, transport of metabolites, and consumption of carbon, nitrogen, sulfur, phosphoric acid, hydrogen and oxygen.(Item 62)53. The method of item 53, wherein the Homo sapiens physiological function is selected from the group consisting of proteolysis, amino acid degradation, purine degradation, pyrimidine degradation, lipid degradation, fatty acid degradation and cofactor degradation. ..(Item 63)53. The method of item 53, wherein the data structure comprises a set of linear algebraic equations.(Item 64)53. The method of item 53, wherein the data structure comprises a matrix.(Item 65)53. The method of item 53, wherein the flux distribution is determined by a linear program.(Item 66)A data structure that associates multiple Homo sapiens reactants with multiple Homo sapiens reactions, the data structure being: (a) Multiple Homo sapiens reactions and the step of identifying multiple Homo sapiens reactants that are substrates and products of the Homo sapiens reactants; (b) In the data structure, a step of associating the plurality of Homo sapiens reactants with the plurality of Homo sapiens reactions, wherein each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction. A step comprising, a reactant identified as a product of the reaction, and a stoichiometric coefficient associated with the substrate and the product; (c) The step of determining the binding set for the plurality of Homo sapiens reactions; (d) The process of providing the objective function; (e) A step of determining at least one flux distribution that minimizes or maximizes the objective function when the constraint set is applied to the data representation, and (f) If the at least one flow flux distribution is unpredictable in Homo sapiens physiological function, add or remove the reaction from the data structure, repeat step (e) and repeat the at least one flow. If the bundle distribution is predictable of Homo sapiens physiological function, the process of storing the data structure on a computer-readable medium,A data structure generated by a process that embraces.

<figref num="1">Figure 1 shows a schematic depiction of the hypothetical metabolic network.</figref><figref num="2">FIG. 2 shows the equilibrium and flux constraints (reversible constraints) of a population that can be located on the hypothetical metabolic network shown in FIG.</figref><figref num="3">FIG. 3 shows a chemotactic matrix (S) for the hypothetical metabolic network shown in FIG.</figref><figref num="4">FIG. 4 shows a typical biochemical reaction network in panel A and further shows a typical regulatory control structure for the reaction network in panel A in panel B.</figref>

(Detailed description of the invention) The present invention provides an in silico model that depicts the interconnection between genes in the Homo sapiens genome and their associated reactions and reactants. This model can be used to simulate different aspects of human cell behavior under different normal, pathological, and therapeutic conditions, resulting in therapeutic applications, diagnostic applications, and studies. Provide valuable information for application. The advantage of the model of the present invention is that this model provides a comprehensive approach for simulating and predicting the activity of Homo sapiens cells. This model and method can also be extended to simulate the activity of multiple interacting cells, including organs, physiological systems and metabolism throughout the body.

As an example, the Homo sapiens metabolic model of the present invention is used to determine the effects of changes from aerobic to anaerobic states, such as those that occur in skeletal muscle or tumors during exercise, or the effects of various dietary changes. Can be used to determine. Homo sapiens metabolic models can also be used to determine the outcome of gene deficiency (eg, deficiencies in metabolic enzymes such as phosphofructokinase, phosphoglycerate kinase, phosphoglycerate mutase, lactate dehydrogenase, and adenosine deaminase).

Homo sapiens metabolic models can also be used to select appropriate targets for drug design. Such targets include genes, proteins or reactants that are positively or negatively modified in simulations that provide the desired therapeutic outcome. The models and methods of the present invention can also be used to predict the effect of therapeutic agents or dietary supplements on the cell function of interest. Similarly, this model and method is used to anticipate the desired and undesired side effects of therapeutic agents on cell function interacting with target cells, as well as the desired and desirable effects that may occur in other cell types. Can be used to anticipate negative effects. Therefore, the models and methods of the present invention can make the process of drug development faster and more cost effective than currently available.

The Homo sapiens metabolic model also identifies the presence of reactions or pathways not indicated by current genomic data, as well as to anticipate or confirm the mission of specific biochemical reactions to genes encoding enzymes found in the genome. Can be used to Therefore, this model can be used to guide the study and discovery of processes and has the potential to lead to the identification of new clinically important enzymes, drugs and metabolites.

The model of the present invention is based on a data structure that associates a plurality of Homo sapiens reactants with a plurality of Homo sapiens reactions, and each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction, a reaction generation. Includes stoichiometric coefficients for reactants identified as products as well as substrates and products. The reactions contained in the data structure can be reactions that are common to all or most of Homo sapiens cells (eg, core metabolic reactions), or reactions that are specific to one or more predetermined cell types.

As used herein, the term "Homo sapiens reaction" refers to a substrate-consuming transformation or product-forming transformation that occurs in or by Homo sapiens cells. Intended to mean. The term may include transformations caused by the activity of one or more enzymes genetically encoded by the Homo sapiens genome. This term is also Homo Includes spontaneous transformations in sapiens cells. Conversions included in this term include, for example, nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, isomerization, deaminoization, phosphorylation, methylation, reduction, oxidation. Changes in chemical composition, such as those caused by transport reactions that move reactants from one cell compartment to another. In the case of a transport reaction, the substrates and products of the reaction can be chemically the same, and the substrates and products are distinguished according to their location in a particular cell compartment. Thus, a reaction that transports a chemically unchanged reactant from a first compartment to a second compartment has the reactant as a substrate in the first compartment and as a product in the second compartment. Has a reactant. When used with reference to in-computer models or data structures, it can be understood that the reaction is intended to be a representation of a substrate-consuming chemical transformation or a product-producing chemical transformation.

As used herein, "reactant of Homo sapiens" is intended to mean a chemical that is a substrate or product of a reaction that occurs in Homo sapiens cells, or a reaction that occurs in Homo sapiens cells. To do. The term refers to reactions performed by one or more enzymes encoded by the Homo sapiens genome, reactions that occur in Homo sapiens performed by one or more non-genetic macromolecules, proteins or enzymes, Alternatively, it may contain a substrate or product of a reaction that occurs spontaneously in the cells of Homo sapiens. Metabolites are understood to be reactants within the meaning of this term. When used with reference to in-computer models or data structures, the reactants are intended to be a representation of a chemical that is the substrate or product of a reaction that occurs in a cell of Homo sapiens, or a reaction that occurs in a cell of Homo sapiens. Then it can be understood.

As used herein, the term "substrate" is intended to mean a reactant that can be converted into one or more products by a reaction. The term is chemically altered by, for example, nucleophilic or electrophilic addition reaction, nucleophilic or electrophilic substitution, desorption, dissimilarity, deaminoization, phosphorylation, methylation, reduction, oxidation. Reactants, or, for example, reactants that are repositioned by being transported through a membrane or into a different compartment.

As used herein, the term "product" is intended to mean a reactant that results from a reaction with one or more substrates. The term refers to reactants chemically altered by nucleophilic or electrophilic addition, nucleophilic or electrophilic substitution, elimination, heterogeneity, deaminoization, phosphorylation, methylation, reduction, oxidation. Alternatively, for example, a reactant that has been repositioned by being transported through a membrane or to a different compartment.

As used herein, the term "stoichiometric coefficient" is intended to mean a numerical constant associated with the number of one or more reactants and the number of one or more products in a chemical reaction. Typically, the numbers are integers when indicating the number of molecules of each reactant in an elementally balanced chemical equation that describes the corresponding transformation. However, in some cases (eg, when used in a lump reaction, or to reflect empirical data), the number can take a non-integer value.

As used herein, the term "plurality" is intended to mean at least two reactions or reactants when used with reference to Homo sapiens reactions or Homo sapiens reactants. The term can include any number of Homo sapiens or Homo sapiens reactants, ranging from 2 to the number of naturally occurring reactants or reactions to specific Homo sapiens cells. Thus, for example, the term may include 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 part of the total number of naturally occurring reactions to a particular Homo sapiens cell (eg, at least 20%, 30% of the total number of naturally occurring reactions occurring in a particular Homo sapiens cell). %, 50%, 60%, 75%, 90%, 95%, or 98%).

As used herein, the term "data structure" is intended to mean a physical or logical relationship between data elements and to support a particular data manipulation function. It is designed. The term refers to an enumeration of data elements that can be added, combined, and otherwise manipulated, such as an enumeration of expressions for reactions in which a reactant can be associated with a matrix or network. Can include. The term can also include a matrix that associates data elements from two or more enumerations of information, such as a matrix that associates a reaction with a reactant. The information contained in this term means, for example, a substrate or product of a chemical reaction, a chemical reaction that associates one or more products with one or more substrates, a constraint located on the reaction, or a stoichiometric coefficient. Can be done.

As used herein, the term "constraint" is intended to mean an upper or lower bound for a reaction. Boundaries can identify the lowest or highest flow of mass, electrons or energy through the reaction. Boundaries can also identify the direction of the reaction. The boundary can be a constant value, such as zero, infinity, or a number (eg, an integer). Alternatively, the boundary can be a variable boundary value, as described below.

As used herein, the term "variable" is intended to mean the ability to accept any set of values in response to an action by a bound function when used with reference to a bound. When used in the context of binding, the term "function" is intended to be consistent with the meaning of this term as understood in the field of computers or mathematics. The function can be binary. As a result, the change corresponding to the reaction is off or on. Alternatively, a continuous function can be used such that the change at the boundary value corresponds to an increase or decrease in activity. Such an increase or decrease can also be discarded or effectively digitized by a function that can convert a set of values to another (discreat) integer value. Functions included in this term may relate the presence, absence or amount of biochemical reaction network associations such as reactants, reactions, enzymes or genes to boundary values. The functions included in this term can associate the boundary value with the result of at least one reaction in a reaction network that contains a reaction bound by a boundary limit. Functions used in this term can also associate boundary values with environmental conditions such as time, pH, temperature or redox potential.

As used herein, the term "activity", when used with reference to a reaction, is the amount of product produced by the reaction, the amount of substrate consumed by the reaction, or the product produced. It is intended to mean the speed or the speed at which the substrate is consumed. The amount of product produced by the reaction, the amount of substrate consumed by the reaction, or the rate at which the product is produced or the rate at which the substrate is consumed can also be referred to as a flux to the reaction.

As used herein, the term "activity" is intended to mean the magnitude or rate of change from initial state to final state when used with reference to Homo sapiens cells. .. The term refers to, for example, the amount of chemicals consumed or provided by a cell, the rate at which a chemical is consumed or produced by a cell, the amount or rate of cell growth, or the reaction of energy, mass, or electrons. It may include the amount or speed of flow through a particular subset.

The present invention provides a computer-readable medium having a data structure that associates a plurality of Homo sapiens reactions with a plurality of Homo sapiens reactants. Here, each of the Homo sapiens reactions comprises a reactant identified as the substrate of the reaction, a reactant identified as the product of the reaction, and stoichiometric coefficients for the substrate and the product.

Depending on the application, the reaction of multiple Homo sapiens may include a reaction selected from a core metabolic reaction or a peripheral metabolic reaction. As used herein, the term "core", when used with reference to metabolic pathways, glycolysis / gluconeogenesis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, glycogen storage, electrons. It is intended to mean a metabolic pathway selected from the transport chain (ETS), malate / aspartate shuttle, glycerin phosphate shuttle, and cell membrane and mitochondrial membrane transporters. As used herein, the term "peripheral" is intended to mean a metabolic pathway that, when used with reference to a metabolic pathway, involves one or more reactions that are not part of the core metabolic pathway. ..

For each reactant, in any data structure that describes the reaction in which the reactant is consumed or produced, the reactants of multiple Homo sapiens can be associated with the reactions of multiple Homo sapiens. Therefore, a data structure referred to herein as a "reaction network data structure" serves as a representation of a biochemical reaction network or biochemical reaction form. Examples of reaction networks that can be depicted in the reaction network data structures of the present invention are the collection of reactions that make up the core metabolic response of Homo sapiens, or the metabolic response of skeletal muscle cells, as shown in the Examples.

The selection of reactions contained in a particular reaction network data structure from all possible reactions in human cells depends on the cell type and physiological state, pathological state, or therapeutic state being modeled. It depends and can be determined experimentally or from the literature, as described further below.

The reactions involved in a particular network data structure of Homo sapiens can be determined experimentally, for example, using gene expression profiles or protein expression profiles in which the molecular characteristics of the cell are associated with expression levels. Expression or lack of expression of a gene or protein in a cell type can be used to determine whether the reaction is included in the model by association with the expressed gene and / or protein. Therefore, experimental techniques can be used to determine if a gene and / or protein is expressed in a particular cell type, and this information can be used to further determine the cell type of interest. It is possible to determine if a reaction is present in. In this method, a subset of reactions from all possible reactions in human cells are selected to include a set of reactions that depict a particular cell type. The cDNA expression profile has been demonstrated to be useful, for example, for the classification of breast cancer cells (Sorlie et al., Proc. Natl. Acad. Sci. USA 98 (19): 10869-10874 (2001)).

The methods and models of the invention include any Homo sapiens cell type (eg, embryonic stem cells, hematopoietic stem cells, differentiated hematopoietic cells, skeletal muscle cells, myocardial cells, smooth muscle cells, skin cells, nerve cells) at any stage of differentiation. , Kidney cells, lung cells, liver cells, fat cells, and endocrine cells, including, for example, β-pancreatic islet cells, mammary gland cells, adrenal cells, and other specialized hormone-secreting cells.

The methods and models of the present invention may be applied to normal cells or pathological cells. Normal cells exhibiting a variety of physiological activities of interest, including homeostasis, proliferation, differentiation, apoptosis, contraction and motility, can be modeled. Pathological cells can also be modeled, such as genetic or developmental abnormalities, nutritional deficiencies, environmental attacks, infections (eg, infections with bacteria, viruses, protozoa or fungi), neoplasia, Cells that reflect aging, immune or endocrine dysfunction, tissue damage, or any combination of these factors). These pathological cells can be any type of human pathology (eg, various sugar metabolism disorders, lipid or protein metabolism, obesity, diabetes, cardiovascular disease, fibrosis, various cancers, renal failure, immunopathology, etc. Neurodegenerative diseases, as well as the Online Mendelian Inheritance in Man Database (Center for Medical Genetics, Johns Hopkins University (Baltimore, MD) and National Center for Biotechnology) It may indicate various monogenetic metabolic disorders described in the Information, National Library of Medicine (Bethesda, MD).

The methods and models of the present invention are also gene-based, which increases or decreases the expression of cells undergoing therapeutic agitation (eg, cells treated with drugs that target the participating substances in the reaction network, the encoded protein. It can be applied to cells treated with therapeutic agents, as well as cells treated with radiation). As used herein, the term "drug" refers to a compound of any molecular property that has a known or proposed therapeutic function, such as low molecular weight compounds, peptides and other macromolecules. , Peptide mimetics and antibodies, all of which are tagged with cell growth inhibitory, targeted or detectable moieties as needed. It can be. The term "gene-based therapeutic agent" refers to a nucleic acid therapeutic agent, such as an expressible gene having normal or altered protein activity, an antisense compound, a ribozyme, a DNAzyme, an RNA interference compound (RNAi), etc. Can be mentioned. This therapeutic agent contains any reaction network involvement at any cell location (extracellular location involvement, cell surface location involvement, cytoplasmic location involvement, mitochondrial location involvement, and nuclear location involvement. Can be targeted. Experimental data collected on cell response to therapeutic agent treatment (eg, changes in gene expression profile or protein expression profile) can be used to tailor networks for the pathological state of a particular cell type.

In the methods and models of the invention, Homo sapiens cells are in any form (eg, primary cell isolate state, or established cell line state, or whole cell body state, intact organ state, or tissue. It can be applied to Homo sapiens cells when present in the explant state). Thus, this method and model includes cell-cell communication and / or organ-to-organ communication, substrate or neighboring cells (eg, stem cells that interact with mesenchymal cells, or cancer cells that interact with the tissue microenvironment, or normal limbs. The effect of adhering to β-island cells), as well as other interactions associated with multicellular lines, can be taken into account.

Reactants to be used in the reaction network data structures of the present 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 containing multiple molecules, including substrates and products for biological reactions. The plurality of molecules may include molecules found in multiple organisms, thereby forming a universal compound database. Alternatively, the plurality of molecules may be limited to molecules present in a particular organism, thereby forming a biospecific compound database. Each reactant in the compound database can be identified according to the species and the cell compartment in which the reactant resides. Thus, for example, a distinction can be made between glucose in the extracellular compartment and glucose in the cytosol. In addition, each of the reactants can be identified as a metabolite of a primary or secondary metabolic pathway. Identification of reactants as metabolites of the primary or secondary metabolic pathways does not show any chemical distinction between those reactants in the reaction, but such distinction is a visual representation of a large reactant network. Can assist.

As used herein, the term "compartment" is intended to mean a subdivision region containing at least one reactant so that the reactants are in the second region. It has been isolated from at least one other reactant. The subdivisions included in this term can correlate with subdivisions of cells. Thus, the subdivisions included in this term are, for example, the intracellular space of a cell; the extracellular space around the cell; the periplasmic space, the organelle (eg, mitochondria, endoplasmic reticulum, Golgi apparatus, vesicles or nuclei). Internal space; or any subcellular space that is separated from others by membranes or other physical barriers. Subdivision regions can also be created to create virtual boundaries in the reaction network that do not correlate with physical barriers. Virtual boundaries can be created to segment the reactants in the network into different compartments or substructures.

As used herein, the term "substruct rue" is such that some of the information can be manipulated or analyzed separately, separate from other information in the data structure. Is intended to mean some of the information in the data structure. The term is subdivided according to biological function (eg, information related to a particular metabolic pathway (eg, internal flux pathway, exchange flux pathway, central metabolic pathway, peripheral metabolic pathway, or secondary metabolic pathway)). Can include the metabolized part. The term may include subdivided parts according to computer or mathematical principles that allow the analysis or manipulation of certain types of data structures.

The reactions contained in the reaction network data structure can be obtained from a metabolic reaction database containing multiple metabolic reaction substrates, products, and stoichiometry of Homo sapiens. Reactants in the reaction network data structure can be designated as either substrates or products of a particular reaction, each of which has been assigned to the reaction to describe the chemical transformations that occur during that reaction. It has a quantitative coefficient. Each reaction is also described as occurring in either a reversible or irreversible direction. A reversible reaction can be shown as one reaction that operates in both the forward and reverse directions, or two irreversible reactions (one corresponding to the forward reaction and the other corresponding to the reverse reaction). ) Can be decomposed.

Reactions included in the reaction network data structure can include in-system reactions or exchange reactions. Intrasystem reactions are chemically and electrically balanced interconversions of species and transport processes that serve to replenish or excrete relative amounts of specific metabolites. These in-system reactions can be classified as either conversion or translocation. Conversion is a reaction involving distinct pairs of compounds as substrates and compounds as products, while translocation involves reactants located in different compartments. Thus, a reaction that simply transports a metabolite from the extracellular environment to a cytoplasmic sol without altering its chemical composition is simply classified as translocation, while obtaining an extracellular matrix and translating it into a cytoplasmic sol product. The transforming reactions are both translocation and transformation.

Exchange reactions are reactions that make up the source and sink, allowing metabolites to pass through and out of compartments, or through virtual system boundaries. These reactions are included in the model for simulation purposes and indicate the metabolic demand located on Homo sapiens. They may be chemically equilibrated in certain cases, but they are typically out of equilibrium and often have only a single substrate or product. By convention, this exchange reaction is further categorized into demand exchange and input / output exchange reactions.

The metabolic demand located on the Homo sapiens metabolic reaction network can be readily determined from the dry weight composition of cells, which is available in the published literature or can be determined experimentally. Uptake rates and maintenance requirements for Homo sapiens cells can also be available or experimentally determined in the published literature.

Input / output exchange reactions are used to allow extracellular reactants to enter or exit the reaction network represented by the model of the invention. Corresponding input / output exchange reactions can be generated for each of the extracellular metabolites. These reactions are always reversible with the one product produced by the reaction and the product-free metabolites shown as substrates using stoichiometric coefficients. This particular conversion causes the reaction to have a positive flux value (activity level) if the biotransform is produced or removed from the reaction network, and the biotransform is consumed or introduced into the reaction network. If so, the negative flux value It is adapted to allow it to be taken. These reactions are further constrained during the course of the simulation to pinpoint which metabolites are available to the cell and can be excreted by the cell.

Demand exchange reactions are always identified as irreversible reactions involving at least one substrate. These reactions typically include the production of intracellular biotransformers by their metabolic network, or many reactants of equilibrium ratios (eg, in the representation of reactions that result in biomass formation (also referred to as growth)). It is formulated to show the formation of aggregates.

The demand exchange reaction can be introduced for any metabolite in the model of the invention. Most commonly, these reactions are the metabolites (eg, amino acids, nucleotides, phospholipids, and other biomass constituents) required to be produced by the cell for the purpose of producing new cells. Or introduced for metabolites that should be produced for another purpose. Once these biotransformers have been identified, a demand exchange reaction can be generated that identifies the biotransformers as substrates that are irreversible and have a stoichiometric coefficient of 1. Using these specifications, if the reaction is active, the reaction results in the net production of metabolites by the system and meets the possible production demands. Examples of processes that can be shown as demand exchange reactions in reaction network data structures and that can be analyzed in the reactions of the invention include, for example, the production or secretion of individual proteins; individual metabolites (eg, amino acids, vitamins, etc.). The production or secretion of nucleosides (antibiotics or surfactants); the production of ATP for processes that require foreign energy (eg, locomotion); or the formation of biomass constituents.

In addition to these demand exchange reactions located on individual metabolites, demand exchange reactions utilizing multiple metabolites at defined stoichiometric ratios can be introduced. These reactions are called agglutination demand exchange reactions. An example of an agglutination demand reaction simulates co-growth demand or production requirements associated with cell growth located on a cell, for example, by simulating the formation of multiple biomass components simultaneously at a particular cell growth rate. It is a reaction used to simulate.

A virtual reaction network is provided in FIG. 1 to illustrate the above reactions and their interactions. These reactions can be shown in the exemplary data structures shown in FIG. 3 shown below. The reaction network shown in FIG. 1 is a reversible reaction R acting on reactants B and G that occur throughout the compartment indicated by the shaded ellipse.2, And the reaction R that transforms one equivalent of B into two equivalents of F3) Is included. The reaction network shown in FIG. 1 is also an exchange reaction (eg, input / output exchange reaction A).xtAnd Ext) And demand exchange reaction VProliferationIncludes (showing proliferation in response to one equivalent of D and one equivalent of F). Other in-system reactions are translocation and conversion reactions that translocate Reactant A into compartments and convert it to Reactant G.1, And reaction R, which is a transport reaction that translocates reactant E out of the compartment.6Can be mentioned.

The reaction network can be represented as a set of linear algebraic equations that can be represented as the stoichiometric matrix S, where S is the m × n matrix, where m corresponds to the number of reactants or metabolites, and n is the network. Corresponds to the number of reactions that occur within. An example of a stoichiometric matrix showing the reaction network of FIG. 1 is shown in FIG. 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 SmnThe element corresponds to the chemical coefficient of the reactant m in the reaction, indicated as n. This stoichiometric matrix is associated with a sign indicating whether the reactant is a substrate or product of the reaction in the system and the number of equivalents of the reactant consumed or produced by the reaction. R related to reactants involved in individual reactions, according to stoichiometric coefficients with valence2And R3)including. Exchange reaction (eg -ExtAnd -Axt) Is related as well as the stoichiometric coefficient. As exemplified by Reactant E, the same compounds are Internal Reactant (E) and External Reactant (E).External) To transport the compound separately as an exchange reaction (R)6) Can be as related by stoichiometric coefficients -1 and 1, respectively. However, the compound is treated as a separate reactant depending on its compartment location, thus producing an internal reactant (E) but an external reactant (E).External) Does not work for reactions (eg R)5) Are associated by stoichiometric coefficients 1 and 0, respectively. Demand reaction (eg VProliferation) Can also be included in the stoichiometric matrix associated with the substrate by the appropriate stoichiometric coefficient.

As shown in more detail below, stoichiometric matrices provide a convenient form for showing and analyzing reaction networks. This is because stoichiometric matrices can be easily manipulated and network properties can be used to calculate, for example, by using linear programming or general convex analysis. The reaction network data structure determines the activity of one or more reactions with the reactants and the reactants in the manner exemplified above for stoichiometric matrices and using methods such as those exemplified below. It can exhibit various forms as long as it can be associated, in a manner that can be manipulated for. Other examples of reaction network data structures useful in the present invention include binding graphs, lists of chemical reactions, or tables of reaction formulas.

The reaction network data structure can be constructed to include all reactions involved in Homo sapiens metabolism or any part thereof. As part of the Homo sapiens metabolic response that can be included in the reaction network data structure of the present invention, for example, a central metabolic pathway (eg, glycolysis, TCA cycle, PPP or ETS); or a peripheral metabolic pathway (eg, amino acid biosynthesis). Synthesis, amino acid degradation, purine synthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, vitamin biosynthesis or cofactor biosynthesis, transport processes and alternative carbon source metabolism). Examples of individual pathways within the peripheral pathway are shown in Table 1.

Depending on the particular application, the reaction network data structure may include multiple Homo sapiens reactions, including any or all of the reactions listed in Table 1.

For some applications, it may be advantageous to achieve a particular Homo sapiens activity under a particular set of environmental conditions using a reaction network data structure that contains a minimal number of reactions. Reaction network data structures with a minimum number of reactions can be identified by performing the simulation methods described in the alternative modes below, in which different reactions or sets of reactions are systematically removed. And the effect is observed. Accordingly, the present invention provides a computer-readable medium comprising a data structure that associates a plurality of Homo sapiens reactants with a plurality of Homo sapiens reactions, the plurality of Homo sapiens reactions comprising at least 65 reactions. For example, the central metabolic response databases shown in Tables 2 and 3 are sufficient to simulate aerobic and anaerobic metabolism based on multiple carbon sources (including glucose).

Depending on the particular cell type, the physiological condition being tested, the pathological condition being tested, or the therapeutic condition being tested, and the desired activity, the response network data structure will have a smaller number of reactions (eg, at least. Can include 200, 150, 100, or 50 reactions). Reaction network data structures with relatively few reactions can offer the advantage of reducing computational time and resources required to perform simulations. If desired, reaction network data structures with a particular reaction subset may be made or used, in which reactions not related to a particular simulation are omitted. Alternatively, a larger number of reactions may be included to increase the accuracy or molecular details of the methods of the invention, or to adapt to a particular application. Thus, the reaction network data structure is the number of reactions that occur in or by Homo sapiens from at least 300, 350, 400, 450, 500, 550, 600 or more reactions, or Homo. It may include up to the desired number of reactions to simulate the activity of the full set of reactions that occur in sapiens. Response network data structures that are substantially complete with respect to the metabolic response of Homo sapiens offer the advantage of being associated with a wide range of states to be simulated, while response network data structures that include fewer metabolic responses , Limited to a specific subset of states to be simulated.

Homo sapiens reaction network data structures occur in or by Homo sapiens, and do not occur in another organism (eg, Saccharomyces cerevisiae), by that other organism, naturally or after manipulation, 1 It can contain more than one reaction. It is understood that the Homo sapiens reaction network data structure of a particular cell type can also contain one or more reactions that occur in another cell type. Adding such heterologous reactions to the reaction network data structures of the invention predicts the outcome of heterologous gene transfer and heterologous protein expression, eg, when designing in vivo gene therapy approaches and exobibo gene therapy approaches. Can be used in the above method.

The reaction contained in the reaction network data structure of the present invention can be a metabolic reaction. Reaction network data structures also include other types of reactions (eg, regulatory reactions, signal transduction reactions, cell cycle reactions, developmental process control reactions, apoptosis-related reactions, reactions involved in responses to hypoxia, cell-cell interactions or cells. -Reactions involved in responses to substrate interactions, reactions involved in protein synthesis and its regulation, reactions involved in gene transcription and translation and their regulation, and reactions involved in the assembly of cells and their subcellular components) Can be constructed to include.

Reaction indicators used in reaction network data structures or those data structures (eg, available in metabolic reaction databases, as described above) are annotated to include information about a particular reaction. obtain. The reaction is, for example, the substitution of the reaction into a protein, macromolecule, or enzyme that carries out the reaction, the substitution of a gene encoding the protein, macromolecule, or enzyme, the Enzyme Commission Number (EC) number of a particular metabolic reaction. , The reaction subset to which the reaction belongs, a citation to the informed reference, or the level of confidence that the reaction is likely to occur in Homo sapiens can be annotated. The computer-readable medium of the present invention may include a genetic database containing annotated reactions. Such information may be obtained during the process of building the metabolic response database or model of the invention as described below.

As used herein, the term "gene database" refers to one or more macromolecules that either assign a reaction to one or more macromolecules that carry out the reaction, or to carry out the reaction. It is intended to mean a computer-readable medium containing at least one reaction, annotated to substitute one or more encoding nucleic acids. A genetic database can contain multiple reactions, some or all of which are annotated. As a note, for example, the name of the macromolecule; the substitution of the function into the macromolecule; the substitution of the organism containing or producing the macromolecule; the substitution of the subcell position for the macromolecule; the reaction is carried out. Substitution of conditions under which a macromolecule is regulated with respect to being, expressed, or degraded; substitution of a cellular component that regulates a macromolecule; amino acid or nucleotide sequence for that macromolecule; or in a genome database Any other notes found on macromolecules in (eg, Genbank (ncbi.nlm.gov), a site maintained by NCBI, Kyoto Encyclopedia of Genes and Genomes (KEGG) (www.genome.ad.jp/kegg/), protein database SWISS-PROT (ca.expasy.org/sprot/), LocusLink database maintained by NCBI (www.ncbi.nlm.nih.gov) / LocusLink /), a note that can be found in the Enzyme Nomenclature database (www.chem.qmw.ac.uk/iubmb/enzyme/) maintained by GPMoss of Queen Mary and Westfield College in the United Kingdom). ..

The gene database of the present invention may include a substantially complete collection of genes or open reading frames in Homo sapiens, or a substantially complete collection of macromolecules encoded by the Homo sapiens genome. Alternatively, the gene database may contain a portion of a gene or open reading frame in Homo sapiens, or a portion of a macromolecule encoded by the Homo sapiens genome (eg, a portion containing substantially all metabolic genes or macromolecules). Can include. That portion is at least 10%, 15%, 20%, 25%, 50%, 75%, 90%, or of the gene or open reading frame encoded by the Homo sapiens genome, or the macromolecule encoded therein. It can be 95%. The genetic database also contains at least a portion of the nucleotide sequence for Homo sapiens (eg, Homo). It may contain macromolecules encoded by at least 10%, 15%, 20%, 25%, 50%, 75%, 90%, or 95% of the sapiens genome. Thus, the computer-readable medium of the invention may include at least one reaction for each macromolecule encoded by a portion of the Homo sapiens genome.

The in-computer Homo sapiens model of the present invention is an iterative process that includes the steps of gathering information about specific reactions to be added to the model, showing those reactions in the reaction network data structure, and performing preliminary simulations. A set of bindings is located in the reaction network and its output is evaluated to identify errors in that network. Errors in the network (eg, gaps resulting in unnatural accumulation or unnatural consumption of a particular metabolite) can be identified as follows, and simulations are repeated until the desired performance of the model is achieved. Will be done. An exemplary method for building an iterative model is provided in Example I.

Accordingly, the present invention provides a method for creating a data structure in a computer reading medium that associates a plurality of Homo sapiens reactants with a plurality of Homo sapiens reactions. This method involves (a) identifying multiple Homo sapiens reactions and multiple Homo sapiens reactants that are substrates and products of those Homo sapiens reactions; (b) data on the multiple Homo sapiens reactants. In the structure, a step of associating the multiple Homo sapiens reactions with each of the Homo sapiens reactions is a reactant identified as a substrate for the reaction, a reaction identified as a product of the reaction, and the substrate. Steps including the stoichiometric coefficients associated with and their products; (c) the step of making a binding set for the multiple Homo sapiens reactions; (d) the step of providing the objective function; (e) the binding set The step of determining at least one flux distribution that minimizes or maximizes the objective function when is applied to the data structure; and (f) that at least one flux distribution is Homo. If it is not a precursor to sapiens physiology, add a reaction to or delete the reaction from that data structure, repeat step (e), and at least one flux of that is the physiology of Homo sapiens. If precursor, it includes the step of storing the data structure in a computer-readable medium.

The information to be included in the data structures of the present invention can be gathered from a variety of sources, including, for example, annotated genomic sequence information and biochemical literature.

Sources of annotated human genome sequence information include, for example, KEGG, SWISS-PROT, LocusLink, Enzyme Nomenclature database, International Human Genome Sequencing Consortium and commercial databases. KEGG contains a wide range of information, including a significant amount of metabolic remodeling. The genomes of 63 organisms can be accessed here, and gene products are grouped by coordinated functions and are often indicated by maps (eg, enzymes involved in glycolysis are grouped together). Will be). The map is a biochemical pathway template showing the enzymes that connect the biotransformers for different parts of metabolism. These common pathway templates are tailored for a given organism by highlighting the given enzyme on the template identified in the genome of the given organism. Enzymes and metabolites, when accessed, are active and give rise to useful information about stoichiometry, structure, aliases, etc.

SWISS-PROT contains detailed information about protein function. Accessible information includes gene aliases and gene product aliases, function, structure and sequence information, and related literature references.

LocusLink contains general information about the locus in which a gene is located, as well as the associated tissue specificity, cell location, and association of its gene product in various disease states.

The Enzyme Nomencalture database can be used to compare the gene products of two organisms. Often, the gene names of genes with similar functions in more than one organism are irrelevant. If this is the case, the EC (Enzyme Commission) number can be used as a primary indicator of gene product function. The information in the Enzyme Nomenclature database has also been published with five appendices to date, Enzyme Nomenclature (Academic Press, San Diego, Califoenia, 1992), all found in the European Journal of Biochemistry (Blackwell Science, Malden, MA). Is done.

Sources of biochemical information include, for example, general sources related to metabolism, resources specifically related to human metabolism, and resources related to biochemistry, physiology, and pathology of specific human cell types. , Can be mentioned.

The sources of general metabolism-related information used to generate the human response databases and models described herein are JG Salway, Metabolism at a Glance, 2nd Edition, Blackwell Science, Malden, MA (1999) and TM Devlin, Textbook of Biochemistry with Clinical Correlations, 4th Edition, John Wiley and Sons, New York, NY (1997). Human metabolism-specific resources include JRBronk, Human Metabolism: Functional Diversity and Integration, Addison Wesley Longman, Essex, England (1999).

References used in combination with the skeletal muscle metabolism models and simulations described herein include R. Maughan et al., Biochemistry of Exercise and Training, Oxford University Press, Oxford, England (1997), and S. Carpenter. Et al., Pathology of Skeletal Muscle, 2nd Edition, References on muscle pathology such as Oxford University Press, Oxford, England (2001), and muscle metabolism that can be found in the Journal of Physiology (Cambridge University Press, Cambridge, England). More specific literature on this was given.

In the process of developing an in-computer model of Homo sapiens metabolism, the types of data that can be considered include, for example, biochemical information, which is information related to the experimental characterization of chemical reactions (which is often related to reactions). Indicates the chemologic theory of the protein and its reaction, or indirectly indicates the presence of the reaction occurring within the cell extract); genetic information (which is to perform experimental identification and biochemical events. Information related to the genetic characterization of genes that have been shown to encode specific proteins of interest); Genomic information (this involves proteins that perform biochemical events via computer sequence analysis. Linked information related to open reading frame identification and function substitution); Physiological information (which is the result of overall cell physiology, health characteristics, substrate utilization, and phenotypic classification (which is specific). Information related to the assimilation or catabolism of compounds used to estimate the presence of biochemical events (particularly in translocation)); as well as modeling information (which is herein). Homo sapiens cells using methods such as those described in, which provide predictions about the state of the reaction, whether or not the reaction is necessary to meet a particular demand located on the metabolic network. Information generated through the process of simulating the activity of). Further information relating to multicellular organisms that may be considered includes cell type-specific gene expression information or condition-specific gene expression information, which can be experimentally, eg, by gene array analysis, or expression sequence tags. It can be determined from (EST) analysis or from the biochemical and physiological literature described above.

Most of the reactions that occur in the Homo sapiens reaction network are catalyzed by enzymes / proteins, which are produced through transcription and translation of genes found in chromosomes in cells. The remaining reactions either occur spontaneously or through non-enzymatic processes. In addition, the reaction network data structure may include reactions that add or remove steps from a particular reaction pathway. For example, the reaction can be added to optimize or improve the performance of the Homo sapiens model, taking into account the empirically observed activity. Alternatively, the reaction can be deleted to remove intermediate steps in the pathway if the intermediate steps are not required to model the flux through the pathway. For example, if the pathway involves three non-equilibrium steps, those reactions can be combined or added together to result in a net reaction, thereby preserving its reaction network data structure. It can reduce the memory required for the operation and the computer resources required for manipulating its data structure.

Reactions resulting from the activity of the enzyme encoded by the gene can be obtained from a genomic database that lists the genes identified from genomic sequencing and subsequent genomic annotation. Genome annotations consist of the location of open reading frames and the substitution of functions from homology or empirically determined activity to other known genes. Such genomic databases may be obtained through public or private databases containing annotated Homo sapiens nucleic acid or protein sequences. If desired, the model developer may perform network restructuring, as described, for example, in Covert et al., Trends in Biochemical Sciences 26: 179-186 (2001) and Palsson, WO 00/46405, and its genes. And the model content relationship between the protein and the reaction can be established.

When a reaction is added to a reaction network data structure or metabolic reaction database, it has a known or putative association between the proteins / enzymes that enable / catalyze the reaction and the associated genes encoding these proteins. Reactions can be identified by annotation. Therefore, the appropriate relationship between all of those reactions and their associated proteins and / or related genes can be substituted. These relationships can be used to acquire a non-linear relationship between the gene and the protein, and a non-linear relationship between the protein and the reaction. In some cases, one gene encodes one protein, which then carries out one reaction. However, often there are multiple genes required to produce an active enzyme complex, and often there are multiple reactions that can be performed by one protein or multiple proteins that can perform the same reaction. These relationships acquire the logic (ie, "and (AND)" or "or" OR "relationships" in the relationship. By annotating the metabolic response database with these relationships, they can be used to perform simulations or Homo sapiens activity, as well as the effect of adding or removing specific reactions at the reaction level. In the putative situation, it is also possible to determine the effect of addition or removal at the genetic or protein level.

The reaction network data structures of the present invention are numerous Homo independent of any knowledge or annotation of the identity of the protein performing this reaction or the gene encoding that protein. It can be used to determine the activity of one or more reactions in the reaction of sapiens. Models annotated by gene or protein identity can include reactions in which the protein or encoding gene is not assigned. While the majority of reactions in the cell's metabolic network are related to genes in the organism's genome, there are also a significant number of reactions included in models with no known genetic association. Such reactions are based on other information not necessarily related to genetics (eg, biochemical-based or cell-based measurements, or theoretical considerations based on observed biochemical or cell activity). Can be added to the reaction database. For example, there are numerous reactions that can either occur spontaneously or not be possible with proteins. Moreover, the occurrence of specific reactions in cells for which no relevant protein or genetics has currently been identified can be demonstrated during the process of model building by the iterative model building method of the present invention.

If desired, the reaction in the reaction network data structure or reaction database can be annotated into the subsystem. These reactions can be subdivided according to biological criteria. This biological criterion is mathematical, for example, facilitating the manipulation of models that follow conventionally identified metabolic pathways (such as glycolysis and amino acid metabolism) or incorporate or manipulate these reactions. Follow criteria or computational criteria. Methods and criteria for subdividing the reaction database are described by Schilling et al., J. Theor. Biol. 203: 249-283 (2000), and Schuster et al., Bioinformatics. 18: 351-361 (2002), described in more detail. The use of subsystems can be advantageous for many analytical methods, such as extreme path analysis, and can make the management of model components easier. Substitution of reactions can be achieved without affecting the use of the entire model for simulation, but substitution of reactions to subsystems can be useful in performing various types of analysis. It may allow the user to explore the reaction in the subsystem of. Thus, the reaction network data structure can be any number (eg, 2 or more subsystems, 5 or more subsystems, 10 or more subsystems, 25 or more subsystems or 50 or more subsystems). Can include the desired subsystem of.

Reactions in reaction network data structures or metabolic reaction databases can be annotated by values that indicate the belief that the reaction will occur in Homo sapiens cells. The level of confidence can be, for example, the amount and form of function that supports the available data. This data can be in a variety of formats, including published literature, documented test results, or computational analysis results. In addition, this data may provide direct or indirect evidence for the presence of chemical reactions in cells based on genetic, biochemical and / or physiological data.

The present invention further provides computer readable media including: (a) a data structure that associates multiple Homo sapiens reactants with multiple Homo sapiens reactants, wherein each of the multiple Homo sapiens reactions is here. , Data structures and (b) constraints on multiple Homo sapiens reactions, including reactants identified as the substrate of the reaction, reactants identified as the product of the reaction, and stoichiometric coefficients associated with the substrate and product. set.

Bindings can be placed on the value of any fluid in the metabolic network using a binding set. These constraints can be representative of the minimum or maximum possible flux through a given reaction, which can result from a limited amount of enzyme present. In addition, binding can determine the direction or reversibility of any or the mobile flux of the reaction in the reaction network data structure. Based on the in vivo environment in which Homo sapiens lives, the metabolic resources available to cells for biosynthesis of essential molecules can be determined. Making it possible to activate the corresponding transport flux provides in-computer Homo sapiens with inputs and outputs for the substrates and by-products produced by its metabolic network.

Returning to the virtual reaction network shown in Figure 1, constraints can be placed on each reaction in the typical format shown in Figure 2, as follows. Bindings are provided in a format that can be used to bind the reactions of the chemical quantity theory matrix shown in FIG. The format for bindings used for matrices or in linear programming is: bj vj aj: j = 1 .... n (Equation 1) Can be expressed as a linear inequality such as, where vjIs the metabolic flux vector, bjIs the minimum flux value and ajIs the maximum flux value. Therefore, ajCan take a finite value that presents the maximum possible flux through a given reaction, or bjCan take a finite value that presents the minimum possible flux through a given reaction. In addition, if you choose to leave a particular reversible reaction or mobile flux to operate in a forward and reverse fashion, the reaction R in FIG.2As shown for bjTo negative infinity, ajBy setting to positive infinity, the flux can remain unconstrained. When the reaction proceeds only in the forward reaction, the reaction R in FIG.1, R3, R4, R5And R6As shown for bjIs 0, while ajTakes positive infinity. As an example, to simulate the event of genetic deficiency or non-expression of a particular protein, the flux through all of the corresponding metabolic reactions for the gene or protein in question is a.jAnd bjBy setting to 0, it is reduced to 0. In addition, if you want to simulate the absence of a particular growth substrate, ajAnd bjBy setting to 0, the corresponding migrating flux that allows the metabolites to enter the cell can be simply constrained. On the other hand, if the substrate is only allowed to enter or leave the cell via a transport mechanism, the corresponding flux can be constrained to properly reflect this scenario.

The ability of the reaction to occur actively depends on a number of additional factors that go beyond mere substrate effectiveness. These factors (which may be represented as variable bindings in the models and methods of the invention) include, for example, the presence of cofactors required to stabilize proteins / enzymes, the presence of enzymatic inhibitors and activators, or Absence, active formation of proteins / enzymes through translation of corresponding mRNA transcripts, transcription of related genes, or chemical signals and / or proteins that assist in controlling these processes and these The process ultimately determines whether a chemical reaction can occur in an organism). Homo Of particular importance in the regulation of sapiens cell types is the implementation of the paracrine and endocrine signaling pathways that control cell activity. In these cases, the cell secretes signal molecules, which can be carried to distant fields and act on distant targets (Endocline signaling) or act as local mediators (Endocline signaling). Parakline signaling). Examples of endocrine signaling molecules include hormones (eg, insulin), while examples of paracrine signaling molecules include neurotransmitters (eg, acetylcholine). These molecules induce cellular responses through a signaling cascade, which influence the activity of biochemical reactions within the cell. Control can be represented in the in-computer Homo sapiens model by providing variable binding (described below).

Accordingly, the present invention provides a computer-readable medium that includes: (a) a data structure that associates multiple Homo sapiens reactants with multiple Homo sapiens reactants, each of which is a reaction. A set of constraints for a data structure and (b) a number of reactions, including reactants identified as substrates for, reactants identified as products of the reaction, and stoichiometric coefficients associated with the substrate and product. , This binding set contains variable bindings for regulated reactions.

As used herein, the term "regulated" has a reaction or variable binding that, when used with respect to a reaction in a data structure, experiences a altered flux due to a change in the value of the binding. It is intended to mean a reaction.

As used herein, the term "regulatory reaction" is intended to mean a chemical transformation or interaction (altering the activity of a protein, macromolecule or enzyme). A chemical transformation or interaction can directly alter the activity of a protein, macromolecule or enzyme (eg, occurs when the protein, macromolecule or enzyme is modified after translation), or the protein, macromolecule or It can indirectly alter the activity of an enzyme (eg, when a chemical transformation or binding event leads to altered expression of a protein, macromolecule or enzyme). Thus, transcriptional or translational regulatory pathways can indirectly alter proteins, macromolecules or enzymes or related reactions. Similarly, indirect regulatory responses can include reactions that result from downstream constructs or associations in the regulatory response network. Data structures or in-computer Homo When used with respect to the sapiens model, the term is intended to mean a first reaction that is associated with a second reaction by function, and that function is bound in that second reaction through that second reaction. The flux is changed by changing the value.

As used herein, the term "regulatory data structure" is intended to mean an event, reaction or network of reactions (activating or inhibiting a reaction), the expression of which is manipulated or analyzed. Exists in the format to get. The event that activates the 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 slows down the rate or level of activity for the reaction. Reactions that can be represented by regulatory data structures include, for example, reactions that control the expression of proteins and then carry out reactions such as transcription and translation reactions, phosphorylation, dephosphorylation, prenylation, methylation, oxidation. Or reactions that result in post-translational modifications of proteins or enzymes, such as covalent modifications, reactions that process proteins or enzymes, such as removal of pre-sequences or pro-sequences, proteins or enzymes. Reactions that decompose proteins or reactions that result in association of proteins or enzymes can be mentioned.

As used herein, the term "regulatory event" is intended to mean a modifier of flux through a reaction that is independent of the amount of reactants available for the reaction. Modifications included in this term can be changes in the presence, absence or amount of the enzyme that carries out the reaction. Modifiers included in this term can be regulatory reactions such as signal transduction reactions or environmental conditions such as changes in pH, temperature, redox capacity or time. When used with respect to in-computer Homo sapiens models or data structures, the regulatory event is intended to be representative of flux modifiers through the Homo sapiens reaction, which is independent of the amount of reactants available for the reaction. It is understood that it will be done.

The effect of regulation on one or more reactions occurring in Homo sapiens can be predicted using the in-computer Homo sapiens model of the present invention. Modulation can be taken into account in the context of specific conditions considered by providing variable constraints on the response in the in-computer Homo sapiens model. Such bindings constitute condition-dependent bindings. Data structures can be represented as regulatory reactions as Boolean logic statements (Reg-reactions). This variable can take a value of 1 if the reaction is available for use in the reaction network, and can take a value of 0 if the reaction is constrained by certain regulatory features. A series of Boolean statements can then be introduced to mathematically represent regulatory networks, such as those described in Covert et al., J. Theor. Biol. 213: 73-88 (2001). For example, if metabolite A is transferred when metabolite A is a transport reaction (A_in) that inhibits reaction R2 as shown in FIG. 4, the boules law can be expressed as: Reg-R2 = IF NOT (A_in) (Equation 2). This statement indicates that reaction R2 can occur in the absence of reaction A_in (ie, in the absence of metabolite A). Similarly, it is possible to substitute that regulation for variable A, which indicates an amount of A above or below the threshold that results in inhibition of reaction R2. Any function that provides values for the variables corresponding to each of the reactions in the biochemical reaction network can be used to represent a set of regulatory reactions or regulatory reactions in a regulatory data structure. Such functions may include, for example, ambiguous theories, discovery-based descriptions, differential equations or kinetic equations detailing system dynamics.

The reaction constraints placed on the reaction can be incorporated into the in-computer Homo sapiens model using the following general formula: (Reg-Reaction) * bj vj aj* (Reg-Reaction) : (Equation 3) j = l .... n For the example of reaction R2, this equation is written as: (0) * Reg-R2 R2 () * Reg-R2 (Equation 4). Therefore, during the course of the simulation, depending on the presence or absence of metabolite A inside the cell where reaction R2 occurs, the values for the upper flux of reaction R2 range from 0 to infinity, respectively. Can change.

The behavior of the Homo sapiens reaction network is simulated for the conditions considered as shown below, due to the effects of regulatory events or networks taken into account by the binding functions, and the condition-dependent bindings set to appropriate initial values. Can be done.

Although regulation is illustrated above where variable binding depends on the outcome of the reaction in the data structure, multiple variable bindings can be included in the in-computer Homo sapiens model representing the regulation of multiple reactions.

In addition, in the typical cases shown above, the regulatory structure includes general control indicating that the reaction is inhibited by certain environmental conditions. Using this type of general control, it is possible to incorporate molecular mechanisms and supplementary details into regulatory structures that can respond to the determination of the active properties of certain chemical reactions in an organism.

Modulation can also be used to predict the physiological function of Homo sapiens without knowledge of the exact molecular mechanisms involved in the reaction networks that can be simulated and modeled by the models of the invention. Therefore, this model has a causal relationship that is not clear from in vivo observations of the overall regulatory event in the computer or any one reaction in the network, or in vivo observations of specific reactions for which in vivo effects are unknown. Can be used to predict. Such overall regulatory effects may include consequences from overall environmental conditions such as pH, temperature, redox capacity, or changes over time.

The in-computer Homo sapiens models and methods described herein are on any conventional host computer system, such as an Intel.RTM. Microprocessor-based host computer system, and the Microsoft Windows® operating system. Can be up and running. Other systems that use the UNIX® or LINUX operating system and are based on the IBM.RTM.Microprocessor, DEC.RTM.Microprocessor or Motorola.RTM.Microprocessor are also contemplated. The systems and methods described herein can also be implemented to operate on client server systems and wide area networks, such as the Internet.

Software that implements the methods or models of the invention is Java®, C, C ++, Visual. It can be written in any well-known computer language such as Basic, FORTRAN or COBOL, and can be compiled using any well-known compatibility compiler. The software of the present invention is usually executed from a command stored in memory on the host computer system. The memory or computer readable medium can be a hard disk, floppy (registered trademark) disk, compact disk, magneto-optical disk, random access memory, read-only memory or flash memory. The memory or computer-readable medium used in the present invention can be contained within a single computer or distributed within a network. The network can be any of a number of conventional network systems known in the art, such as local area networks (LANs) or wide area networks (WANs). Client-server environments, database servers and networks that can be used in the present invention are well known in the art. For example, a database server may run and run related database management systems, worldwide web applications and worldwide web servers on operating systems such as UNIX®. Other types of memory and computer readable media are also contemplated to function within the scope of the present invention.

The database or data structure of the present invention may be represented in a markup language format including, for example, a standardized generalized markup language (SGML), a hypertext markup language (HTML) or an extended markup language (XML). Markup languages can be used to tag information stored in the databases or data structures of the invention, thereby providing convenient annotation and data transfer between the database and data structures. In particular, the XML format can be useful for constructing data representations of reactions, reactants and their annotations; for example, on the network or the Internet, for exchanging database content; document object models. It can be useful to use to update individual elements; or to provide differential access to a large number of users for different informational content of the databases or data structures of the invention. .. An editor for writing XML programming methods and XML code is, for example, Ray's "Learning XML" (O'Reilly and). It is known in the art as described in Associates, Sebastopol, CA (2001)).

The set of bindings can be applied to the reaction network data structure to simulate mass flow through the reaction network under a particular set of environmental conditions specified by the binding set. The time constants that characterize metabolic transients and / or metabolic responses are typically very rapid (milliseconds-seconds) compared to the time constants of cell proliferation in hours-days. Because of the unit of), the transient mass balance can be simplified just to consider steady-state behavior. With reference to the example where the reaction network data structure is a stoichiometric matrix, the steady-state mass balance can be applied using the system of linear equations below: S v = 0 (Equation 5) Where S is a stoichiometric matrix as defined above and v is a flux vector. This equation defines the constraints of mass, energy, and redox capacity placed on the metabolic network as a result of stoichiometry. Equations 1 and 5, which represent reaction binding and mass balance, respectively, effectively define the metabolic genotype and the ability and binding of the organism's metabolic capacity. All vectors (v) that satisfy Equation 5 are said to occur in the mathematical kernel of S. Thus, this kernel defines a steady metabolic flux distribution that does not break the constraints of mass, energy or redox equilibrium. Typically, the number of fluxes is greater than the number of mass balance bindings, so multiple flux distributions satisfy the mass balance bindings and occupy kernel. The kernel is further reduced in size by applying the reaction bindings shown in Equation 1 which defines the feasible set of metabolic flux distributions and leads to the defined solution space. The points in this space represent the flux distribution and thus the metabolic phenotype for the network. The optimal answer within the range of all set of answers can be determined using mathematical optimization methods when supplied by a defined purpose and binding set. The calculation of any answer constitutes a simulation of the model.

Objectives for human cell activity can be selected. Although the overall purpose of a multicellular organism can be proliferation or regeneration, individual human cell types generally have an even more complex purpose, the seemingly extreme purpose of apoptosis (programmed cell death) (of the organism). It can be beneficial, but obviously not for individual cells). For example, certain cell types may have the purpose of maximizing energy production, while other cell types produce certain hormones, extracellular matrix components, or mechanical properties such as contractile forces. Has the purpose of maximizing. Proliferation and proliferative effects need not be taken into account when cell regeneration is as slow as human skeletal muscle. In other cases, the biomass constituents and growth rate can be incorporated into a "maintenance" type flux, setting precursor production to levels consistent with experimental findings rather than optimizing for growth. And different purposes are optimized.

Certain cell types, including cancer cells, may be thought to have the purpose of maximizing cell proliferation. Growth can be defined for biosynthetic requirements based on experimentally defined values such as literature values of biomass constituents or values obtained as described above. Thus, biomass production can be defined as an exchange reaction that removes intermediate metabolites in appropriate ratios and can be expressed as an objective function. In addition to excretion of intermediate metabolites, this reaction flux can be formed to utilize energy molecules such as ATP, NADH and NADPH, thereby incorporating any maintenance requirements that must be achieved. obtain. This new reaction flux then becomes another constraint / equilibrium equation that the system must satisfy as an objective function. Using the stoichiometric matrix of Figure 3 as an example, adding such a constraint is an additional column V to represent the flux that describes the production demand placed on the metabolic system in the stoichiometric matrix.ProliferationIs similar to adding. Setting this new flux as an objective function and requiring the system to maximize the value of this flux for a given set of constraints on all other fluxes is therefore of the organism. It is a method of simulating proliferation.

Continuing the example of a stoichiometric matrix that applies a constraint set to a reaction network data structure can be explained as follows. The solution to Equation 5 can be formulated as an optimization problem in which a flux distribution is found that minimizes a particular objective. Mathematically, this optimization problem can be described as: Minimize Z (Equation 6) Where z = Σci Vi (Equation 7) Where Z is the weight c in this linear combinationiUsing the metabolic flux viThe purpose is expressed as a linear combination. The optimization problem can also be described as an equivalent maximization problem (ie, by changing the sign of Z). Any command (including, for example, primary programming commands) for solving an optimization problem can be used.

The computer system of the present invention may further include a user interface capable of accepting expressions of one or more reactions. The user interface of the present invention may also be able to send at least one command that qualifies a data structure, binding set or command in order to apply the binding set to a data representation, or combination. This interface can be a graphic user interface with graphical means for making selections such as menus or dialog boxes. This interface may be arranged in a hierarchical screen that can be accessed by making a selection from the main screen. This user interface provides access to other databases useful in the present invention (eg, metabolic reaction databases) or links to other databases that have information related to the reaction or reactant in the reaction network data structure. Or Homo May provide access to sapiens physiology. The user interface may also use the model of the invention to present the results of a graph display or simulation of the reaction network.

Once the initial set of reaction network data structures and bindings has been created, this model can be tested by preliminary simulations. During the preliminary simulation, gaps in the network, or "dead ends" where metabolites can be produced but not consumed, or "dead ends" where metabolites can be consumed but not produced can be identified. Based on the results of preliminary simulations, areas of metabolic remodeling that require further reaction can be identified. Determining these gaps can be easily calculated through appropriate queries of reaction network data structures and does not require the use of simulation strategies, but simulations can be an alternative approach to positioning such gaps. ..

In the preliminary simulation test and model component fine-tuning process, existing models can meet basic demands such as their ability to produce the required biomass constituents, and the basics of the particular cell type modeled. It is subjected to a series of functional tests to determine if it is possible to generate predictions about physiological properties. The more preliminary tests performed, the higher the quality of the generated model. Typically, most of the simulations used at this stage of development can be a single optimization. A single optimization can be used to calculate a single flux distribution that demonstrates how metabolic resources are pathwayd from the solution of a single optimization problem. The optimization problem can be solved using primary programming as demonstrated in the examples below. The result can be thought of as a presentation of the flux distribution on the reaction map. Temporary reactions can be added to the network to determine if they should be included in a model based on modeling / simulation requirements.

Once the model of the invention is sufficiently complete with respect to the content of the reaction network data structure according to the above criteria, this model can be used to simulate the activity of one or more reactions in the reaction network. The results of the simulation can be displayed in a variety of formats, including, for example, tables, graphs, reaction networks, flux distribution maps or phenotypic phase plane graphs.

Therefore, the present invention provides a method for predicting the physiological function of Homo sapiens. This method involves the following steps: (a) providing a data structure that associates multiple Homo sapiens reactants with multiple Homo sapiens reactions, each of which serves as a substrate for the reaction. Steps; (b) Provide binding sets for multiple Homo sapiens reactions, including the reactants identified, the reactants identified as the product of the reaction, and the stoichiometric coefficients associated with this substrate and this product. Steps to: (c) Provide objective functions, and (d) Determine at least one flux distribution that minimizes or maximizes this objective function when applied to this data structure. The process of predicting the physiological function of Homo sapiens.

A method of predicting the physiological function of Homo sapiens can include the following steps: (a) providing a data structure that associates multiple Homo sapiens reactants with multiple Homo sapiens reactions, the Homo sapiens reaction. Each of these contains a reactant identified as a substrate for the reaction, a reactant identified as a product of the reaction, and a stoichiometric coefficient that associates this substrate with this product, and less of these reactions. A step; (b) providing a binding set for multiple reactions, wherein the binding set comprises a variable binding for this regulated reaction; (c). ) The process of providing a condition-dependent value for this variable constraint; (d) the process of providing an objective function, and (e) minimizing this objective function if this constraint set is applied to this data structure. Alternatively, the step of determining at least one flow flux distribution to maximize, thereby predicting the physiological function of Homo sapiens.

As used herein, the term "physiological function" is intended to mean the activity of Homo sapiens cells as a whole when used with respect to Homo sapiens. The activity included in this term can be the magnitude or rate of change from the initial state of Homo sapiens cells to the final state of human cells. Activities included in this term can be, for example, proliferation, energy production, redox equivalent production, biomass production, generation, or consumption of carbon nitrogen, sulfur, phosphate, hydrogen or oxygen. The activity is also a specific reaction in Homo sapiens cells or Homo It can be the output of a particular response that is determined or predicted in virtually any context of a response that affects virtually all of the reactions that occur in sapiens cells (eg, muscle contraction). Examples of specific reactions included in this term are biomass precursor production, protein production, amino acid production, purine production, pyrimidine production, lipid production, fatty acid production, cofactor production, or biotransformers. Transport. Physiological functions may include appearance characteristics that appear from the whole but not from the sum of the parts (this part is observed alone) (see, eg, Palsson, Nat.Biotech 18: 1147-1150 (2000)). That).

The physiological function of the Homo sapiens response can be determined using phase plane analysis of the flow flux distribution. A phase plane is a representation of a feasible set that can be represented in two or three dimensions. As an example, two parameters that describe growth conditions such as substrate and oxygen uptake rates can be defined as two axes in two-dimensional space. The optimal flux distribution is the reaction network data structure, as described above, for all points in this plane by repeatedly solving linear programming problems while adjusting the exchange flux that defines the two-dimensional space. And it can be calculated from the binding set. A finite number of patterns of utilization of qualitatively different metabolic pathways can be identified in such planes, and lines can be drawn to distinguish between these regions. The boundaries that define these areas are, for example, Chvatal, Linear Programming New York, WH Freeman and It can be determined using the potential value of linear optimization as described in Co. (1983). These regions are called regions of stationary latent value structure. The latent value defines the real value of each reactant to the objective function as a number that is either negative, 0, or positive, and is graphed by the uptake rate represented by the x and y axes. If the latent value becomes 0 as the capture rate value changes, then a qualitative shift exists in the optimal reaction network.

One boundary line in the representational phase plane is defined as the line of optimization (LO). This line represents the optimal relationship between each metabolic flux. LO can be identified by changes in the x-axis flux and calculation of the optimal y-axis flux, with an objective function defined as the proliferative flux. From the phenotypic phase plane analysis, the conditions can be determined under the condition that the desired activity is optimal. The maximum uptake rate provides a finite area of plot, which is the predicted result of the reaction network in the environmental conditions represented by the binding set. Similar analysis can be performed in multiple dimensions, where each dimension on the plot corresponds to a different capture rate. These and other methods for using phase plane analysis (eg, the method described in Edwards et al., Biotech Bioeng. 77: 27-36 (2002)) use the in-computer Homo sapiens model of the present invention. Can be used to analyze the results of simulations.

The physiological function of Homo sapiens can also be determined using a reaction map that presents a flux distribution. Homo sapiens reaction maps can be used to view reaction networks at various levels. In the case of a cellular metabolic reaction network, the reaction map may include the entire reaction complement representing the overall perspective. Alternatively, the response map may focus on specific regions of metabolism (eg, regions corresponding to the reaction subsystems described above), or even individual pathways or reactions.

Accordingly, the present invention provides an apparatus that produces an expression of the physiological function of Homo sapiens, which expression is produced by a process involving the following steps: (a) multiple Homo sapiens reactants and multiple expressions. A step of providing a data structure associated with a Homo sapiens reaction, wherein each Homo sapiens reaction is identified as a substrate for the reaction, a reactant identified as a product of the reaction, and this substrate and this. Steps that include the quantification coefficients associated with the product; (b) Steps that provide a binding set for multiple Homo sapiens reactions; (c) Steps that provide an objective function; (d) This binding set is this data The process of determining at least one flow flux distribution that minimizes or maximizes this objective function when applied to a structure, thereby predicting the physiological function of Homo sapiens, as well as (e). The process of generating an expression of the activity of one or more Homo sapiens reactions.

The methods of the invention include, for example, amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, alternative carbon source metabolites and It can be used to determine the activity of multiple Homo sapiens reactions, including metabolic transport. In addition, these methods can be used to determine the activity of one or more of the reactions described above or those listed in Table 1.

The methods of the invention can be used to determine the phenotype of variants of Homo sapiens. The activity of one or more Homo sapiens reactions can be determined using the methods described above, and this reaction network data structure lacks the reaction associated with one or more genes that occur in Homo sapiens. Alternatively, this method can be used to determine the activity of one or more Homo sapiens reactions when non-naturally occurring reactions are added to the reaction network data structure in Homo sapiens. Gene deficiency can also be represented in the model of the invention by binding the flux to zero throughout the reaction, thereby allowing this reaction to remain in the data structure. Therefore, simulations can be performed to predict the effect of adding a gene to Homo sapiens or removing the gene from Homo sapiens. These methods can be particularly useful for determining the effect of adding or deleting genes encoding gene products that carry out reactions in peripheral metabolic pathways.

Targeting for drugs or any other drug that affects Homo sapiens function can be predicted using the methods of the invention. Such predictions can be generated by eliminating the reaction to simulate total inhibition or prevention by the drug or drug. Alternatively, partial inhibition or reduction in the activity of a particular reaction can be predicted by performing the method with varying constraints. For example, the reduced activity is such that the metabolic flux vector of the target reaction reflects a finite maximum or minimum flux value corresponding to the level of inhibition.jValue or bjBy changing the value, it can be introduced into the model of the present invention. Similarly In addition, the effect of activating the reaction can be predicted by initiating the reaction, or by performing this method on a reaction network data structure that lacks a particular reaction by increasing the activity of the reaction, or So that the metabolic flux vector of the target reaction reflects the maximum or minimum flux value corresponding to the level of activation, ajValue or bjIt can be predicted by changing the value. This method can be particularly useful for identifying targets for peripheral metabolic pathways.

Once a reaction has been identified in which activation or inhibition produces the desired effect on Homo sapiens function, the enzyme or macromolecule that carries out the reaction in Homo sapiens, or the gene that expresses those enzymes or macromolecules. Can be identified as a target for a drug or other drug. Candidate compounds for the target identified by the methods of the invention can be isolated or synthesized using known methods. Such methods for isolating or synthesizing compounds include, for example, rational design based on known properties of the target (eg, DeCamp et al., Protein Engineering Principles and Practice, Cleland and Craik ed., Wiley-Liss, New York, pp.467-506 (1996)), screening targets against a combinatorial library of compounds (eg, Houghten et al., Nature, 354,84-86 (1991); Dooley et al. Science, 266). , 2019-2022 (1994) (described repetitive approach), or R. Houghten et al. PCT / US91 / 08694 and US Pat. No. 5,556,762 (described position scanning approach), or Combining both to obtain a focused library can be mentioned. Those skilled in the art may understand or routinely determine the assay conditions used in screening based on the characteristics of the target or in activity assays known in the art.

Candidate drugs or agents, whether identified by the methods described above or by other methods known in the art, are using the in-computer Homo sapiens model or method of the invention. Can be evaluated. The effect of a candidate drug or drug on the physiological function of Homo sapiens can be predicted based on its activity (measured in vitro or in vivo) on the target in the presence of the candidate drug or drug. This activity reflects the effect measured on the activity of this reaction of the candidate drug or drug by adding the reaction to this model, removing the reaction from this model, or binding the reaction within this model. It can be represented in the in-computer Homo sapiens model by adjusting to. By performing simulations under these conditions, the overall effect of the candidate drug or drug on the physiological function of Homo sapiens can be predicted.

The methods of the invention can be used to determine the effect of one or more environmental components or conditions on the activity of Homo sapiens cells. As shown above, exchange reactions can be added to the uptake of environmental components, the release of components into the environment, or reaction network data structures that meet other environmental demands. The effect of an environmental component or environmental condition is adjusted so that the metabolic flux vector of its exchange reaction target reaction reflects a finite maximum or minimum flux value corresponding to the effect of the environmental component or environmental condition a.jValue or bjFurther research can be done by running the simulation on the values. Environmental components can be, for example, alternative carbon sources or carbon metabolites that can be taken up and metabolized when added to the environment of Homo sapiens cells. The environmental component can be, for example, a combination of components present in the minimum medium composition. Therefore, these methods can also be used to determine the optimal or minimal medium composition that can support a particular activity of Homo sapiens.

The present invention further provides a method of determining a set of environmental components that achieves the desired activity for Homo sapiens. This method involves: (a) providing a data structure that associates multiple Homo sapiens reactants with multiple Homo sapiens reactions, each of which identifies each of the Homo sapiens reactions as a substrate for the reaction. Steps; (b) Provide binding sets for multiple Homo sapiens reactions, including the reactants to be produced, the reactants identified as the product of the reaction, and the stoichiometric coefficients that associate this substrate with this product. Steps; (c) Applying this constraint set to this data representation, thereby determining the activity of one or more Homo sapiens reactions; (d) Steps (a)-steps (c). According to one or more Homo A step of determining the activity of the sapiens reaction, in which this binding set contains an upper or lower limit in the amount of environmental components, as well as (e) repeating steps (a)-(c) with the altered binding set. Here, the activity determined in step (e) is improved as compared with the activity determined in step (d).

The following examples describe the invention, but are intended to be non-limiting.

(Example I) This example demonstrates the construction of a general purpose Homo sapiens metabolic response database, a Homo sapiens core metabolic response database, and a Homo sapiens muscle cell metabolic response database. This example also shows a repetitive model-building process used to produce the Homo sapiens core metabolism model and the Homo sapiens muscle cell metabolism model. A general purpose Homo sapiens reaction database was created from the genomic database and biochemical literature. This reaction database, shown in Figure 1, contains the following information: Locus ID-The locus number of the gene found on the LocusLink website. Gene abbreviations-Various abbreviations used for genes. Reaction Stoichiometry-Includes all metabolites and reaction directions and reversibility. EC-Enzyme Commission number. Further information contained in the general-purpose response database (not shown in Table 1) is in the Salway (1999) chapter where relevant responses are found; intracellular when the response first occurs in a given compartment. Placement; SWISS PROT identifier that can be used to position a gene record in SWISS PROT; the full name of the gene at a given locus; Mendelian Inheritance in Man (MIM) data associated with the gene; and the gene in a particular tissue The histological type when first expressed was listed. Overall, genes encoding 1130 metabolic enzymes (or transporters) were placed in a general-purpose reaction database. Fifty-nine reactions in a general-purpose reaction database were identified and included (currently without genomic annotation) based on biological data, as found in Salway (1999). Following preliminary simulation tests and fine-tuning of model components, 10 additional reactions (not listed in the biochemical literature or genomic annotations) were placed in the reaction database. These 69 reactions are shown at the end of Table 1. From the general-purpose Homo sapiens reaction database shown in Table 1, core metabolic reactions and some amino acid metabolism reactions and fatty acid metabolism reactions (JG Salway, Metabolism at a Glance, 2nd edition, Blackwell Science, Malden, MA (1999)) A core metabolic reaction database has been established, including (described in Chapters 1, 3, 4, 7, 9, 9, 10, 13, 17, 18, and 44). The core metabolic response database contains 211 unique reactions consisting of 737 genes in the Homo sapiens genome. The core metabolic response database was used to generate the core metabolic model described in Example II, but not in its entirety. To enable modeling of muscle cells, the core response database was extended to include 446 unique responses consisting of 889 genes in the Homo sapiens genome. This skeletal muscle metabolic response database was used to create the skeletal muscle metabolic model described in Example II. Once the core cell metabolic response database and muscle cell metabolic response database were edited, the responses were represented as metabolic network data structures or "stoichiometric input files". For example, the core metabolic network data structure shown in Table 2 contains 33 reversible reactions, 31 irreversible reactions, a 97-column matrix and 52 unique enzymes. Each reaction in Table 2 is a substrate (negative number) and product (positive number); stoichiometry; the name of each reaction (word following 0); and the reaction is reversible (R following the reaction name). Represented to indicate whether or not. Metabolites appearing in mitochondria are indicated by "m", and metabolites appearing in extracellular space are indicated by "ex". In order to perform preliminary simulations or simulate physiological conditions, a set of inputs and outputs must 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, substitute the non-zero uptake value of glucose using the notation shown in Table 2 and aim for ATP production. Was maximized as. Network execution was tested using the flux balance analysis method by optimizing the set of constraints specified in a given objective function and input file. The model was iteratively prepared by testing the results of the simulation and making appropriate changes. Using this iterative procedure, we generated two metabolic response networks that represent human core metabolism and human skeletal muscle cell metabolism. (Example II) This example demonstrates how human metabolism can be accurately simulated using the Homo sapiens core metabolism model. The human core metabolic reaction database shown in Table 3 was used in the human core metabolism simulation. This reaction database contains a total of 65 reactions, including glycolysis, pentose phosphate pathway, tricitrate circuit, oxidative phosphorylation, glycogen storage, malate-aspartate shuttle, glycerol phosphate shuttle, and plasma transporters and mitochondrial membranes. Covers the classical biochemical pathways of transporters. This reaction network is divided into three compartments: the cytosol cleft, the mitochondrial cleft, and the extracellular cleft. The total number of metabolites in the network is 50, 35 of which also appear in mitochondria. This core metabolic network consists of 250 human genes. To perform simulations using the core metabolic network, network characteristics (eg, P / O ratios) were specified using Salway (above) (1999) as a reference. Oxidation of NADH via the electron transfer system (ETS) is set to produce 2.5 ATP molecules (ie, a P / O ratio of 2.5 for NADH), and FADH.2Oxidation of 1.5 ATP molecules (ie FADH)2It was set to produce a P / O ratio of 1.5). Aerobic and anaerobic metabolism were simulated in-computer using the core metabolism network. Secretion of metabolic by-products was consistent with known physiological parameters. All 12 precursor metabolites (glucose-6-phosphate, fructos-6-phosphate, ribose-5-phosphate, erythrose-4-phosphate, triosephosphate, 3-phosphoglycerate, phosphoenolpyruvate, The maximum production of pyruvate, acetyl CoA, α-ketoglutaric acid, succinyl CoA, and oxaloacetate) was tested and no value was found that exceeded its theoretical production. Maximum ATP production was also tested in cytosol and mitochondria. Salway (1999) reports that in the absence of a membrane proton-coupled transport system, energy production is 38 ATP molecules per glucose molecule, or 31 ATP molecules per glucose molecule. A core metabolic model demonstrating the same values is described by Salway (1999). Energy production in mitochondria was determined to be 38 ATP molecules per glucose molecule. This is equivalent to energy production in the absence of a proton-coupled transporter across the mitochondrial membrane, as all protons are utilized only in oxidative phosphorylation. In the cytosol, the energy production was calculated to be 30.5 molecules of ATP per molecule of glucose. This value reflects the cost of metabolic exchange across the mitochondrial membrane (described by Salway (above) (1999)). (Example III) This example shows how human muscle cell metabolism is simulated using the Homo sapiens muscle cell metabolism model under different physiological and different pathological conditions. As described in Example I, novel functions (eg, fatty acid synthesis and β-oxidation, triacylglycerol formation and phospholipids) are included so that the core metabolism model also includes all major reactions that occur in skeletal cells. Formation, as well as amino acid metabolism) was added and extended to the classical metabolic pathways found in the core pathway. Simulations were performed using the muscle cell response database shown in Table 4. The biochemical reaction was repartitioned into cytosol and mitochondrial compartments. In order to simulate the physiological behavior of human skeletal muscle cells, it was necessary to define an objective function. Proliferation of skeletal muscle cells occurs on a time scale of hours to days. However, the time scale of interest in the simulation is in the order of minutes to 10 minutes, reflecting the time limit of metabolic exchange between exercises. Therefore, contraction (defined as energy production and related to it) was chosen to be an objective function and did not impose any further constraints to represent growth demand in the cell. Twelve physiological cases (Table 5) and five disease cases (Table 6) were tested to study and test network behavior. Metabolite inputs and outputs were specified as identified in Table 5, and maximum energy production and metabolite secretion were calculated and counted. (Table 5)<tables num="5"><img file="JP2010146578A_D0001.tif" /></tables> (Table 6)<tables num="6"><img file="JP2010146578A_D0002.tif" /></tables> Skeletal muscle models were tested for the utilization of different carbon sources available during different stages of exercise and food starvation (Table 5). The network by-product secretion in the aerobic to anaerobic change is qualitatively compared to the physiological consequences of exercise and food starvation, with the same general characteristics such as reduced fermentative by-product secretion and energy production. Found to show. Network behavior was also tested in five disease cases (Table 6). Test cases were selected based on their physiological relevance to model predictive ability. Briefly, McCardle's disease is characterized by a dysfunction of glycogenolysis. Tarui's disease is characterized by a deficiency of phosphofructokinase. The remaining diseases tested are characterized by deficiencies in metabolic enzymes (phosphoglycerate kinase, phosphoglycerate mutase, and lactate dehydrogenase). In each case, flux changes and metabolite by-product secretion, glycogen (for changes from aerobic to anaerobic) and phosphocreatine (as a single carbon source to the network) and pyruvate, lactic acid, And albumin (just as a metabolic by-product) tested and left the system intact. The corresponding deficient enzyme was limited to 0 to simulate disease cases. In all cases, a severe reduction in energy production was demonstrated during exercise. This represents the condition of the disease as seen in clinical cases. Various publications are referenced throughout this application. The disclosures of these publications, in their entirety, are incorporated herein by reference in order to more fully describe the state of the art to which the invention belongs. Although the present invention has been described with reference to the examples provided above, it should be understood that various modifications can be made without departing from the spirit of the present invention. Therefore, the present invention is limited only by the claims.<tables num="1-001"><img file="JP2010146578A_D0003.tif" /></tables><tables num="1-002"><img file="JP2010146578A_D0004.tif" /></tables><tables num="1-003"><img file="JP2010146578A_D0005.tif" /></tables><tables num="1-004"><img file="JP2010146578A_D0006.tif" /></tables><tables num="1-005"><img file="JP2010146578A_D0007.tif" /></tables><tables num="1-006"><img file="JP2010146578A_D0008.tif" /></tables><tables num="1-007"><img file="JP2010146578A_D0009.tif" /></tables><tables num="1-008"><img file="JP2010146578A_D0010.tif" /></tables><tables num="1-009"><img file="JP2010146578A_D0011.tif" /></tables><tables num="1-010"><img file="JP2010146578A_D0012.tif" /></tables><tables num="1-011"><img file="JP2010146578A_D0013.tif" /></tables><tables num="1-012"><img file="JP2010146578A_D0014.tif" /></tables><tables num="1-013"><img file="JP2010146578A_D0015.tif" /></tables><tables num="1-014"><img file="JP2010146578A_D0016.tif" /></tables><tables num="1-015"><img file="JP2010146578A_D0017.tif" /></tables><tables num="1-016"><img file="JP2010146578A_D0018.tif" /></tables><tables num="1-017"><img file="JP2010146578A_D0019.tif" /></tables><tables num="1-018"><img file="JP2010146578A_D0020.tif" /></tables><tables num="1-019"><img file="JP2010146578A_D0021.tif" /></tables><tables num="1-020"><img file="JP2010146578A_D0022.tif" /></tables><tables num="1-021"><img file="JP2010146578A_D0023.tif" /></tables><tables num="1-022"><img file="JP2010146578A_D0024.tif" /></tables><tables num="1-023"><img file="JP2010146578A_D0025.tif" /></tables><tables num="1-024"><img file="JP2010146578A_D0026.tif" /></tables><tables num="1-025"><img file="JP2010146578A_D0027.tif" /></tables><tables num="1-026"><img file="JP2010146578A_D0028.tif" /></tables><tables num="2-001"><img file="JP2010146578A_D0029.tif" /></tables><tables num="2-002"><img file="JP2010146578A_D0030.tif" /></tables><tables num="2-003"><img file="JP2010146578A_D0031.tif" /></tables><tables num="3-001"><img file="JP2010146578A_D0032.tif" /></tables><tables num="3-002"><img file="JP2010146578A_D0033.tif" /></tables><tables num="4-001"><img file="JP2010146578A_D0034.tif" /></tables><tables num="4-002"><img file="JP2010146578A_D0035.tif" /></tables><tables num="4-003"><img file="JP2010146578A_D0036.tif" /></tables><tables num="4-004"><img file="JP2010146578A_D0037.tif" /></tables>

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