Method and system for modeling cellular metabolism

• US 20020168654A1
• Filed: 01/10/2002
• Published: 11/14/2002
• Est. Priority Date: 01/10/2001
• Status: Active Grant

• Current Assignee: Penn State Research Foundation (Pennsylvania State University)
• Original Assignee: Penn State Research Foundation (Pennsylvania State University)
• Inventors: Burgard, Anthony P., Maranas, Costas D.
• Granted Patent: US 7,711,490 B2
• 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 5/20 Probabilistic models

Abstract

This invention relates to methods and systems for in silico or bioinformatic modeling of cellular metabolism. The invention includes methods and systems for modeling cellular metabolism of an organism, comprising constructing a flux balance analysis model, and applying constraints to the flux balance analysis model, the constraints selected from the set consisting of: qualitative kinetic information constraints, qualitative regulatory information constraints, and differential DNA microarray experimental data constraints. In addition, the present invention provides for computational procedures for solving metabolic problems.

Claims

1. A method for modeling cellular metabolism of an organism, comprising:

constructing a flux balance analysis model;

applying constraints to the flux balance analysis model, the constraints selected from the set consisting of;

qualitative kinetic information constraints, qualitative regulatory information constraints, and differential DNA microarray experimental data constraints.

2. The method of claim 1 wherein the constraints are logic constraints selected to protect against violation of a kinetic or regulatory barrier.

3. The method of claim 1 wherein the constraints are connectivity restraints.

4. The method of claim 1 further comprising the step of applying mixed-integer linear programming to solve for a desired metabolic outcome.

5. The method of claim 1 further comprising the step of solving for a desired metabolic outcome.

6. A method for modeling cellular metabolism of an organism that improves upon a flux balance analysis model, comprising:

constructing the flux balance analysis model; and

applying a plurality of logic constraints to the flux balance analysis model.

7. The method of claim 6, further comprising selecting the set of logic constraints to protect against violation a kinetic or regulatory barrier.

8. The method of claim 6 wherein the logic constraints are defined by a relationship between changes in reaction fluxes and metabolic concentrations.

9. The method of claim 6 wherein the logic constraints are defined by a relationship between reaction fluxes and transcript levels of gene coding.

10. The method of claim 6 wherein the logic constraints are represented by binary variables.

11. The method of claim 10 wherein a first binary variable represents the presence of a reaction and a second binary variable represents the absence of a reaction.

12. The method of claim 6 further comprising applying a computational procedure to identify a minimal set of metabolic reactions.

13. The method of claim 12 further comprising selecting a growth rate, and wherein the step of applying a computational procedure is applying a computational procedure to identify the minimal set of metabolic reactions capable of supporting the growth rate.

14. The method of claim 6 further comprising the step of applying mixed-integer linear programming to solve for a desired metabolic outcome.

15. The method of claim 6 further comprising the step of solving for a desired metabolic outcome.

16. The method of claim 15 further comprising engineering a change in an organism based on the desired metabolic outcome.

18. The method of claim 16 wherein the growth rate target is a biomass target production rate.

17. A method for determining a reduced genome, comprising:

selecting a minimal set of reactions from a set of metabolic reactions that meets a growth rate target;

mapping enzymes catalyzing the minimal set of reactions to a corresponding set of coding genes, the corresponding set of coding genes defining a reduced genome.

19. A system for modeling cellular metabolism of an organism, comprising:

a flux balance analysis model;

a plurality of constraints applied to the flux balance analysis model, the constraints selected from the set consisting of;

qualitative kinetic information constraints, qualitative regulatory information constraints, and differential DNA microarray experimental data constraints.