Methods and systems to identify operational reaction pathways

• US 20040072723A1
• Filed: 02/14/2003
• Published: 04/15/2004
• Est. Priority Date: 10/15/2002
• Status: Active Grant

• Current Assignee: Regents of the University of California (University of California)
• Original Assignee: Regents of the University of California (University of California)
• Inventors: Herrgard, Markus, Palsson, Bernhard O., Covert, Markus W.
• Granted Patent: US 7,734,420 B2
• Time in Patent Office: Days
• US Class Current: 514/1
• CPC Class Codes: G01N 33/5005 involving human or animal cells immunoassay G01N33/56966; immunoassays of protozoa G01N33/56905; protozoa in screening assays C12Q1/025
• CPC Class Codes: G16B 25/00 ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
• CPC Class Codes: G16B 25/10 Gene or protein expression profiling; Expression-ratio estimation or normalisation
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• 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

Abstract

The present invention provides a method for identifying an operational reaction pathway of a biosystem. The method includes (a) providing a set of systemic reaction pathways through a reaction network representing said biosystem; (b) providing a set of phenomenological reaction pathways of said biosystem, and (c) comparing said set of systemic reaction pathways with said set of phenomenological reaction pathways, wherein a pathway common to said sets is an perational reaction pathway of said biosystem. Also described is a method of refining a biosystem reaction network; a method of reconciling biosystem data sets; a method of determining the effect of a genetic polymorphism on whole cell function; and a method of diagnosing a genetic polymorphism-mediated pathology.

Claims

1. A method of identifying an operational reaction pathway of a biosystem, comprising:

(a) providing a set of systemic reaction pathways through a reaction network representing said biosystem,

(b) providing a set of phenomenological reaction pathways of said biosystem,

(c) comparing said set of systemic reaction pathways with said set of phenomenological reaction pathways, wherein a pathway common to said sets is an perational reaction pathway of said biosystem.

2. The method of claim 1, wherein said biosystem is a prokaryotic cell, or biological process thereof.

3. The method of claim 2, wherein said prokaryotic organism is selected from the group consisting of E. coli, B. subtilis, H. influenzae and H. pylori.

4. The method of claim 2, wherein said biological process is metabolism.

5. The method of claim 1, wherein said biosystem is a eukaryotic cell, or biological process thereof.

6. The method of claim 5, wherein said prokaryotic organism is selected from the group consisting of S. cerevisiae and H. sapiens.

7. The method of claim 5, wherein said biological process is metabolism.

8. The method of claim 1, wherein step (a) comprises determining a set of extreme pathways of said reaction network, and providing said set.

9. The method of claim 1, wherein step (a) further comprises reconstructing said reaction network.

10. The method of claim 1, wherein step (a) further comprises assigning biochemical functions to open reading frames of a genome.

11. The method of claim 1, wherein step (a) further comprises determining open reading frames of a sequence of a genome.

12. The method of claim 1, wherein step (a) further comprises determining the sequence of a genome.

13. The method of claim 1, wherein step (b) comprises analyzing gene expression data.

14. The method of claim 13, wherein step (b) further comprises determining said gene expression.

15. The method of claim 1, wherein step (b) comprises analyzing protein expression.

16. The method of claim 15, wherein step (b) further comprises determining said protein expression.

17. The method of claim 1, wherein step (b) comprises analyzing metabolite production.

18. The method of claim 17, wherein step (b) further comprises determining said metabolite production.

19. The method of claim 1, wherein step (b) comprises analyzing reaction usage.

20. The method of claim 1, wherein step (b) further comprises determining said reaction usage.

21. The method of any of claims 13-20, wherein said data is analyzed using a method selected from the group consisting of clustering analysis, singular value decomposition, principal component analysis and multivariable time series analysis.

22. The method of claim 21, wherein said data is analyzed using singular value decomposition.

23. A method of refining a biosystem reaction network, comprising:

(a) providing a mathematical representation of a biosystem;

(b) determining differences between observed behavior of a biosystem and in silico behavior of said mathematical representation of said biosystem under similar conditions;

(c) modifying a structure of said mathematical representation of said biosystem;

(d) determining differences between said observed behavior of said biosystem and in silico behavior of said modified mathematical representation of said biosystem under similar conditions, and

(e) repeating steps (d) and (e) until behavioral differences are minimized, wherein satisfaction of a predetermined accuracy criteria indicates an improvement in said biosystem reaction network.

24. The method of claim 23, further comprising the steps:

(f) determining behavior of said biosystem under different conditions, and

(g) repeating steps (b) through (e) of claim 23 under said different conditions.

25. The method of claim 24, further comprising repeating steps (f) and (g) until said minimized behavioral differences are exhausted, wherein said improved biosystem reaction network representing an optimal biosystem reaction network.

26. The method of claim 23 or 24, wherein said improved biosystem reaction network identifies a new regulatory network.

27. The method of claim 23 or 24, wherein said improved biosystem reaction network identifies a regulatory state.

28. The method of claim 23, wherein said mathematical representation of said biosystem comprises a regulatory network.

29. A method of reconciling biosystem data sets, comprising:

(a) providing a first reaction network reconstructed from legacy data comprising a plurality of hierarchical reaction categories;

(b) providing a second reaction network obtained from empirical data, and

(c) determining a consistency measure between said hierarchical reaction categories in said first reaction network and elements in said second reaction network, wherein a high degree of said consistency measure for said hierarchical reaction categories indicates the validity of said first reaction network or a subcomponent thereof.

30. The method of claim 29, wherein said empirical data comprises primary data.

31. The method of claim 29, wherein said empirical data comprises nucleic acid expression data.

32. The method of claim 29, wherein said legacy data comprises secondary data.

33. The method of claim 29, wherein said legacy data further comprises data obtained from published reports and databases.

34. The method of claim 29, wherein said hierarchical reaction categories comprises reactions or regulatory events.

35. The method of claim 34, wherein said hierarchical regulatory events comprise pair-wise regulatory interactions, target-regulatory units, and regulons.

36. The method of claim 29, further comprising identifying valid subcomponents of said reaction network.

37. The method of claim 29, wherein said reaction network comprises a network selected from a metabolic reaction network, a regulatory reaction network, a transcriptional reaction network and a genome-scale reaction network, or any combination thereof.

38. The method of claim 29, wherein said reaction network further comprises a mathematical or statistical representation.

39. A method of determining the effect of a genetic polymorphism on whole cell function, comprising:

(a) generating a reaction network representing a biosystem with a genetic polymorphism-mediated pathology;

(b) applying a biochemical or physiological condition stressing a physiological state of said reaction network, and

(c) determining a sensitivity to said applied biochemical or physiological condition in said stressed physiological state compared to a reaction network representing a normal biosystem, wherein said sensitivity is indicative of a phenotypic consequence of said genetic polymorphism-mediated pathology.

40. The method of claim 39, wherein said biochemical or physiological condition is selected from a change in flux load, pH, reactants, and products.

41. The method of claim 39, wherein said biochemical or physiological condition comprises an oxidative load or an energy load.

42. A method of diagnosing a genetic polymorphism-mediated pathology, comprising:

(a) applying a biochemical or physiological condition stressing a physiological state of a reaction network representing a biosystem with a genetic polymorphism-mediated pathology, said applied biochemical or physiological condition correlating with said genetic polymorphism-mediated pathology, and

(b) measuring one or more biochemical or physiological indicators of said pathology within said reaction network, wherein a change in said one or more biochemical or physiological indicators in said stressed state compared to an unstressed physiological state indicates the presence of a genetic polymorphism corresponding to said pathology.

43. The method of claim 42, wherein said biochemical or physiological condition is selected from a change in flux load, pH, reactants, and products.

44. The method of claim 42, wherein said biochemical or physiological condition comprises an oxidative load or an energy load.