Compositions and methods for modeling bacillus subtilis metabolism

• US 20030224363A1
• Filed: 03/19/2002
• Published: 12/04/2003
• Est. Priority Date: 03/19/2002
• Status: Abandoned Application

• Current Assignee: Genomatica Incorporated
• Original Assignee: Genomatica Incorporated
• Inventors: Park, Sung M., Palsson, Bernhard O., Schilling, Christophe H.
• Application Number:
  US10/102,022
• Publication Number:
  US 20030224363A1
• Time in Patent Office: Days
• US Class Current: 435/6
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/10 Boolean models
• CPC Class Codes: G16B 50/00 ICT programming tools or database systems specially adapted for bioinformatics
• CPC Class Codes: Y02A 90/10 Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

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

Claims

1. A computer readable medium or media, comprising:

(a) a data structure relating a plurality of Bacillus subtilis reactants to a plurality of Bacillus subtilis reactions,

wherein each of said Bacillus subtilis reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product,

wherein at least one of said Bacillus subtilis reactions is annotated to indicate an associated gene;

(b) a gene database comprising information characterizing said associated gene;

(c) a constraint set for said plurality of Bacillus subtilis reactions, and

(d) commands for determining at least one flux distribution that minimizes or maximizes an objective function when said constraint set is applied to said data representation, wherein said at least one flux distribution is predictive of a Bacillus subtilis physiological function.

2. The computer readable medium or media of claim 1, wherein said plurality of Bacillus subtilis reactions comprises at least one reaction from a peripheral metabolic pathway.

3. The computer readable medium or media of claim 2, wherein said peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes.

4. The computer readable medium or media of claim 1, wherein said Bacillus subtilis physiological function is selected from the group consisting of growth, energy production, redox equivalent production, biomass production, production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, production of a cell wall component, transport of a metabolite, and consumption of carbon nitrogen, sulfur, phosphate, hydrogen or oxygen.

5. The computer readable medium or media of claim 1, wherein said Bacillus subtilis physiological function is selected from the group consisting of degradation of a protein, degradation of an amino acid, degradation of a purine, degradation of a pyrimidine, degradation of a lipid, degradation of a fatty acid, degradation of a cofactor and degradation of a cell wall component.

6. The computer readable medium or media of claim 1, wherein said data structure comprises a set of linear algebraic equations.

7. The computer readable medium or media of claim 1, wherein said data structure comprises a matrix.

8. The computer readable medium or media of claim 1, wherein said commands comprise an optimization problem.

9. The computer readable medium or media of claim 1, wherein said commands comprise a linear program.

10. The computer readable medium or media of claim 1, wherein at least one reactant in said plurality of Bacillus subtilis reactants or at least one reaction in said plurality of Bacillus subtilis reactions is annotated with an assignment to a subsystem or compartment.

11. The computer readable medium or media of claim 10, wherein a first substrate or product in said plurality of Bacillus subtilis reactions is assigned to a first compartment and a second substrate or product in said plurality of Bacillus subtilis reactions is assigned to a second compartment.

12. The computer readable medium or media of claim 1, wherein a plurality of said Bacillus subtilis reactions is annotated to indicate a plurality of associated genes and wherein said gene database comprises information characterizing said plurality of associated genes.

13. A computer readable medium or media, comprising:

(a) a data structure relating a plurality of Bacillus subtilis reactants to a plurality of Bacillus subtilis reactions,

wherein each of said Bacillus subtilis reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate-and said product,

wherein at least one of said Bacillus subtilis reactions is a regulated reaction;

(b) a constraint set for said plurality of Bacillus subtilis reactions, wherein said constraint set includes a variable constraint for said regulated reaction, and

(c) commands for determining at least one flux distribution that minimizes or maximizes an objective function when said constraint set is applied to said data representation, wherein said at least one flux distribution is predictive of a Bacillus subtilis physiological function.

14. The computer readable medium or media of claim 13, wherein said variable constraint is dependent upon the outcome of at least one reaction in said data structure.

15. The computer readable medium or media of claim 13, wherein said variable constraint is dependent upon the outcome of a regulatory event.

16. The computer readable medium or media of claim 13, wherein said variable constraint is dependent upon time.

17. The computer readable medium or media of claim 13, wherein said variable constraint is dependent upon the presence of a biochemical reaction network participant.

18. The computer readable medium or media of claim 17, wherein said participant is selected from the group consisting of a substrate, product, reaction, protein, macromolecule, enzyme and gene.

19. The computer readable medium or media of claim 13, wherein a plurality of said reactions are regulated reactions and said constraints for said regulated reactions comprise variable constraints.

20. A computer readable medium or media, comprising:

(a) a data structure relating a plurality of Bacillus subtilis reactants to a plurality of Bacillus subtilis reactions,

wherein each of said Bacillus subtilis reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product;

(b) a constraint set for said plurality of Bacillus subtilis reactions, and

(c) commands for determining at least one flux distribution that minimizes or maximizes an objective function when said constraint set is applied to said data representation, wherein said at least one flux distribution is predictive of Bacillus subtilis growth.

21. A method for predicting a Bacillus subtilis physiological function, comprising:

(a) providing a data structure relating a plurality of Bacillus subtilis reactants to a plurality of Bacillus subtilis reactions,

wherein each of said Bacillus subtilis reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product,

wherein at least one of said Bacillus subtilis reactions is annotated to indicate an associated gene;

(b) providing a constraint set for said plurality of Bacillus subtilis reactions;

(c) providing an objective function, and

(d) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, thereby predicting a Bacillus subtilis physiological function related to said gene.

22. The method of claim 21, wherein said plurality of Bacillus subtilis reactions comprises at least one reaction from a peripheral metabolic pathway.

23. The method of claim 22, wherein said peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes.

24. The method of claim 21, wherein said Bacillus subtilis physiological function is selected from the group consisting of growth, energy production, redox equivalent production, biomass production, production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, production of a cell wall component, transport of a metabolite, and consumption of carbon nitrogen, sulfur, phosphate, hydrogen or oxygen.

25. The method of claim 21, wherein said Bacillus subtilis physiological function is selected from the group consisting of glycolysis, the TCA cycle, pentose phosphate pathway, respiration, biosynthesis of an amino acid, degradation of an amino acid, biosynthesis of a purine, biosynthesis of a pyrimidine, biosynthesis of a lipid, metabolism of a fatty acid, biosynthesis of a cofactor, metabolism of a cell wall component, transport of a metabolite and metabolism of a carbon source, nitrogen source, oxygen source, phosphate source, hydrogen source or sulfur source.

26. The method of claim 21, wherein said data structure comprises a set of linear algebraic equations.

27. The method of claim 21, wherein said data structure comprises a matrix.

28. The method of claim 21, wherein said flux distribution is determined by linear programming.

29. The method of claim 21, further comprising:

(e) providing a modified data structure, wherein said modified data structure comprises at least one added reaction, compared to the data structure of part (a), and

(f) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said modified data structure, thereby predicting a Bacillus subtilis physiological function.

30. The method of claim 29, further comprising identifying at least one participant in said at least one added reaction.

31. The method of claim 30, wherein said identifying at least one participant comprises associating a Bacillus subtilis protein with said at least one reaction.

32. The method of claim 31, further comprising identifying at least one gene that encodes said protein.

33. The method of claim 30, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Bacillus subtilis physiological function.

34. The method of claim 21, further comprising:

(e) providing a modified data structure, wherein said modified data structure lacks at least one reaction compared to the data structure of part (a), and

(f) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said modified data structure, thereby predicting a Bacillus subtilis physiological function.

35. The method of claim 34, further comprising identifying at least one participant in said at least one reaction.

36. The method of claim 35, wherein said identifying at least one participant comprises associating a Bacillus subtilis protein with said at least one reaction.

37. The method of claim 36, further comprising identifying at least one gene that encodes said protein that performs said at least one reaction.

38. The method of claim 35, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Bacillus subtilis physiological function.

39. The method of claim 21, further comprising:

(e) providing a modified constraint set, wherein said modified constraint set comprises a changed constraint for at least one reaction compared to the constraint for said at least one reaction in the data structure of part (a), and

(f) determining at least one flux distribution that minimizes or maximizes said objective function when said modified constraint set is applied to said data structure, thereby predicting a Bacillus subtilis physiological function.

40. The method of claim 39, further comprising identifying at least one participant in said at least one reaction.

41. The method of claim 40, wherein said identifying at least one participant comprises associating a Bacillus subtilis protein with said at least one reaction.

42. The method of claim 41, further comprising identifying at least one gene that encodes said protein.

43. The method of claim 40, further comprising identifying at least one compound that alters the activity or amount of said at least one participant, thereby identifying a candidate drug or agent that alters a Bacillus subtilis physiological function.

44. The method of claim 21, further comprising providing a gene database relating one or more reactions in said data structure with one or more genes or proteins in Bacillus subtilis.

45. A method for predicting a Bacillus subtilis physiological function, comprising:

(a) providing a data structure relating a plurality of Bacillus subtilis reactants to a plurality of Bacillus subtilis reactions,

wherein each of said Bacillus subtilis reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product,

wherein at least one of said Bacillus subtilis reactions is a regulated reaction;

(b) providing a constraint set for said plurality of Bacillus subtilis reactions, wherein said constraint set includes a variable constraint for said regulated reaction;

(c) providing a condition-dependent value to said variable constraint;

(d) providing an objective function, and

(e) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, thereby predicting a Bacillus subtilis physiological function.

46. The method of claim 45, wherein said value provided to said variable constraint changes in response to the outcome of at least one reaction in said data structure.

47. The method of claim 45, wherein said value provided to said variable constraint changes in response to the outcome of a regulatory event.

48. The method of claim 45, wherein said value provided to said variable constraint changes in response to time.

49. The method of claim 45, wherein said value provided to said variable constraint changes in response to the presence of a biochemical reaction network participant.

50. The method of claim 49, wherein said participant is selected from the group consisting of a substrate, product, reaction, enzyme, protein, macromolecule and gene.

51. The method of claim 45, wherein a plurality of said reactions are regulated reactions and said constraints for said regulated reactions comprise variable constraints.

52. A method for predicting Bacillus subtilis growth, comprising:

(a) providing a data structure relating a plurality of Bacillus subtilis reactants to a plurality of Bacillus subtilis reactions,

wherein each of said Bacillus subtilis reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product;

(b) providing a constraint set for said plurality of Bacillus subtilis reactions;

(c) providing an objective function, and

(d) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, thereby predicting Bacillus subtilis growth.

53. A method for making a data structure relating a plurality of Bacillus subtilis reactants to a plurality of Bacillus subtilis reactions in a computer readable medium or media, comprising:

(a) identifying a plurality of Bacillus subtilis reactions and a plurality of Bacillus subtilis reactants that are substrates and products of said Bacillus subtilis reactions;

(b) relating said plurality of Bacillus subtilis reactants to said plurality of Bacillus subtilis reactions in a data structure,

wherein each of said Bacillus subtilis reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product;

(c) determining a constraint set for said plurality of Bacillus subtilis reactions;

(d) providing an objective function;

(e) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, and

(f) if said at least one flux distribution is not predictive of a Bacillus subtilis physiological function, then adding a reaction to or deleting a reaction from said data structure and repeating step (e),

if said at least one flux distribution is predictive of a Bacillus subtilis physiological function, then storing said data structure in a computer readable medium or media.

54. The method of claim 53, wherein a reaction in said data structure is identified from an annotated genome.

55. The method of claim 54, further comprising storing said reaction that is identified from an annotated genome in a gene database.

56. The method of claim 53, further comprising annotating a reaction in said data structure.

57. The method of claim 56, wherein said annotation is selected from the group consisting of assignment of a gene, assignment of a protein, assignment of a subsystem, assignment of a confidence rating, reference to genome annotation information and reference to a publication.

58. The method of claim 53, wherein step (b) further comprises identifying an unbalanced reaction in said data structure and adding a reaction to said data structure, thereby changing said unbalanced reaction to a balanced reaction.

59. The method of claim 53, wherein said adding a reaction comprises adding a reaction selected from the group consisting of an intra-system reaction, an exchange reaction, a reaction from a peripheral metabolic pathway, reaction from a central metabolic pathway, a gene associated reaction and a non-gene associated reaction.

60. The method of claim 59, wherein said peripheral metabolic pathway is selected from the group consisting of amino acid biosynthesis, amino acid degradation, purine biosynthesis, pyrimidine biosynthesis, lipid biosynthesis, fatty acid metabolism, cofactor biosynthesis, cell wall metabolism and transport processes.

61. The method of claim 53, wherein said Bacillus subtilis physiological function is selected from the group consisting of growth, energy production, redox equivalent production, biomass production, production of biomass precursors, production of a protein, production of an amino acid, production of a purine, production of a pyrimidine, production of a lipid, production of a fatty acid, production of a cofactor, production of a cell wall component, transport of a metabolite, development, intercellular signaling, and consumption of carbon nitrogen, sulfur, phosphate, hydrogen or oxygen.

62. The method of claim 53, wherein said Bacillus subtilis physiological function is selected from the group consisting of degradation of a protein, degradation of an amino acid, degradation of a purine, degradation of a pyrimidine, degradation of a lipid, degradation of a fatty acid, degradation of a cofactor and degradation of a cell wall component.

63. The method of claim 53, wherein said data structure comprises a set of linear algebraic equations.

64. The method of claim 53, wherein said data structure comprises a matrix.

65. The method of claim 53, wherein said flux distribution is determined by linear programming.

66. A data structure relating a plurality of Bacillus subtilis reactants to a plurality of Bacillus subtilis reactions, wherein said data structure is produced by a process comprising:

(a) identifying a plurality of Bacillus subtilis reactions and a plurality of Bacillus subtilis reactants that are substrates and products of said Bacillus subtilis reactions;

(b) relating said plurality of Bacillus subtilis reactants to said plurality of Bacillus subtilis reactions in a data structure,

wherein each of said Bacillus subtilis reactions comprises a reactant identified as a substrate of the reaction, a reactant identified as a product of the reaction and a stoichiometric coefficient relating said substrate and said product;

(c) determining a constraint set for said plurality of Bacillus subtilis reactions;

(d) providing an objective function;

(e) determining at least one flux distribution that minimizes or maximizes said objective function when said constraint set is applied to said data structure, and

(f) if said at least one flux distribution is not predictive of Bacillus subtilis physiology, then adding a reaction to or deleting a reaction from said data structure and repeating step (e),

if said at least one flux distribution is predictive of Bacillus subtilis physiology, then storing said data structure in a computer readable medium or media.