Method and system for modeling biological systems

• US 20020091666A1
• Filed: 07/03/2001
• Published: 07/11/2002
• Est. Priority Date: 07/07/2000
• Status: Abandoned Application

• Current Assignee: Physiome Sciences, Inc. (EPIX Pharmaceuticals, Inc.)
• Original Assignee: Physiome Sciences, Inc. (EPIX Pharmaceuticals, Inc.)
• Inventors: Rice, John Jeremy, Lett, Gregory Scott
• Application Number:
  US09/898,151
• Publication Number:
  US 20020091666A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 3/004 Artificial life, i.e. computing arrangements simulating life

Abstract

The present invention relates to a method and system for quantitative and semi-quantitative modeling of biological and physiological systems. More specifically, the invention relates to the use of overlays to store and manipulate computational biological models. Also provided by the invention are methods and systems for preparing overlays, methods and systems for creating new computational biological models by applying overlays to old models, and computer program products comprising overlays.

Claims

1. A method for storing multiple computational biological models, said method comprising:

a. selecting a base model from a plurality of computational biological models;

b. computing an overlay for each computational biological model other than the base model;

c. storing said base model; and

d. storing said overlays.

2. The method of claim 1 wherein said base model is selected in order to minimize total storage requirements.

3. The method of claim 1 wherein said base model is selected in order to maximize the number of common model components shared by the base model and the other computational biological models.

4. The method of claim 1 wherein at least one of said overlays is computed by differencing the computational biological model corresponding to said overlay from said base model.

5. The method of claim 1 wherein said computational biological models have been ordered into a defined series, and each overlay is computed by differencing its corresponding computational biological model from the prior computational biological model in the series.

25. A method according claims 1, 6, 7, 11 or 23 wherein said base computational biological model comprises a system of algebraic equations, ordinary differential equations, partial differential equations or combinations thereof.

26. A method according claims 1, 6, 7, 11 or 23 wherein said computational biological models are represented as matrices.

27. A method according claims 1, 6, 7, 11 or 23 wherein said overlays are represented as matrices.

59. A computer program product loadable into the memory of a computer, said product comprising software code portions for performing the steps of any one of claims 1, 6, 7, 11 or 23 when said product is run on said computer.

6. A method for quantitative or semi-quantitative modeling of a biological or physiological system, said method comprising:

a. applying one or more overlays to a base computational biological model to generate a second computational biological model; and

b. running a predictive simulation of said second computational biological model.

8. A method in accordance with claims 6 or 7 wherein said base model is created using traditional modeling methods.

9. A method in accordance with claims 6 or 7 wherein said base model is created using automated model generation techniques.

10. A method in accordance with claim 6 or 7, further comprising the steps of:

running a predictive simulation of said base model; and

comparing the results of the base-model simulation with the results of the simulation of said second computational biological model.

7. A method for quantitative or semi-quantitative modeling of a biological or physiological system, said method comprising:

a. retrieving a base computational biological model;

b. retrieving an overlay;

c. applying said overlay to said base model to generate a new computational biological model; and

d. running a simulation of said new model on a computer.

11. A method for creating an overlay comprising:

a. constructing a base computational biological model;

b. constructing a second computational biological model;

c. comparing the second model with the base model to ascertain the differences between the two models; and

d. computing an overlay based upon the differences between the two models.

12. The method of claim 11 wherein said comparison of the two models is performed at the character-by-character or byte-by-byte level.

13. The method of claim 11 wherein said comparison of the two models is performed at a level of abstraction that reveals true structural or biologically significant differences.

14. The method of claim 11 wherein said second model is constructed by adjusting said base model based upon experimental data.

15. The method of claim 14 wherein said second model construction step includes minimizing an error metric measuring the difference between the predictions made by said second model and said experimental data.

16. The method of claim 15 wherein said error metric is the L2 norm.

17. The method of claim 15 wherein said error-minimization step comprises applying a batch estimator.

18. The method of claim 15 wherein said error-minimization step comprises applying a recursive filter.

19. The method of claim 18 wherein said recursive filter is selected from the group of filters consisting of the least-squares filter, the pseudo-inverse filter, the square-root filter, the Kalman filter, the particle filter, and Jazwinski'"'"'s adaptive filter.

20. The method of claim 18 wherein said filter is a fading-memory filter.

21. The method of claim 20 wherein said filter is a Kalman-type filter.

22. The method of claim 21 wherein said filter is an extended Kalman filter or an unscented Kalman filter.

24. The method of claim 23 wherein said information includes gene-expression data, protein-expression data, or combinations thereof.

28. An overlay incorporated in a computer readable medium created in accordance with the method of claims 15 or 23.

29. The overlay of claim 28, wherein said overlay is represented in an XML format.

30. The overlay of claim 29 wherein said XML format is CellML.

23. A method for creating an overlay comprising:

a. obtaining information or data relevant to a base computational biological model; and

b. computing an overlay based upon the model changes implied by said information or data.

31. An overlay incorporated in a computer readable medium comprising:

means to operate on a computational biological model to introduce at least one change in said model.

32. The overlay of claim 31, wherein said overlay is represented in an XML format.

33. The overlay of claim 32 wherein said XML format is CellML.

35. The system of claim 34 wherein said base model is selected in order to minimize total storage requirements.

36. The system of claim 34 wherein said base model is selected in order to maximize the number of common model components shared by the base model and the other computational biological models.

37. The system of claim 34 wherein at least one of said overlays is computed by differencing the computational biological model corresponding to said overlay from said base model.

38. The system of claim 34 wherein said computational biological models have been ordered into a defined series, and each overlay is computed by differencing its corresponding computational biological model from the prior computational biological model in the series.

34. A system for storing multiple computational biological models, said system comprising:

a. means for selecting a base model from a plurality of computational biological models;

b. means for computing an overlay for each computational biological model other than the base model;

c. means for storing said base model; and

d. means for storing said overlays.

39. A system for quantitative or semi-quantitative modeling of a biological or physiological system, said system comprising:

a. means for applying one or more overlays to a base computational biological model to generate a second computational biological model; and

b. means for simulating said second computational biological model.

41. A system in accordance with claims 39 or 40 wherein said base model is created using traditional modeling methods.

42. A system in accordance with claims 39 or 40 wherein said base model is created using automated model generation techniques.

43. A system in accordance with claims 39 or 40, further comprising the steps of:

running a predictive simulation of said base model; and

comparing the results of the base-model simulation with the results of the simulation of said second computational biological model.

40. A system for quantitative or semi-quantitative modeling of a biological or physiological system, said system comprising:

a. means for retrieving a base computational biological model;

b. means for retrieving an overlay;

c. means for applying said overlay to said base model to generate a new computational biological model; and

d. means for simulating said new model on a computer.

44. A system for creating an overlay comprising:

a. means for constructing a base computational biological model;

b. means for constructing a second computational biological model;

c. means for comparing the second model with the base model to ascertain the differences between the two models; and

d. means for computing an overlay based upon the differences between the two models.

45. The system of claim 44 wherein said comparison of the two models is performed at the character-by-character or byte-by-byte level.

46. The system of claim 44 wherein said comparison of the two models is performed at a level of abstraction that reveals true structural or biologically significant differences.

47. The system of claim 44 wherein said second model is constructed by adjusting said base model based upon experimental data.

48. The system of claim 47 wherein said second model construction step includes minimizing an error metric measuring the difference between the predictions made by said second model and said experimental data.

49. The system of claim 48 wherein said error metric is the L2 norm.

50. The system of claim 48 wherein said error-minimization step comprises applying a batch estimator.

51. The system of claim 48 wherein said error-minimization step comprises applying a recursive filter.

52. The system of claim 51 wherein said recursive filter is selected from the group of filters consisting of the least-squares filter, the pseudo-inverse filter, the square-root filter, the Kalman filter, the particle filter, and Jazwinski'"'"'s adaptive filter.

53. The system of claim 51 wherein said filter is a fading-memory filter.

54. The system of claim 53 wherein said filter is a Kalman-type filter.

55. The system of claim 54 wherein said filter is an extended Kalman filter or an unscented Kalman filter.

57. The system of claim 56 wherein said information includes gene-expression data, protein-expression data, or combinations thereof.

56. A system for creating an overlay comprising:

a. means for obtaining information or data relevant to a base computational biological model; and

b. means for computing an overlay based upon the model changes implied by said information or data.

58. A computer program product comprising at least one overlay stored in a computer usable media in a computer readable format.