Minimally perturbing a gene regulatory network to avoid a disease phenotype: the glioma network as a test case.

Abstract

BACKGROUND:

Mathematical modeling of biological networks is an essential part of Systems Biology. Developing and using such models in order to understand gene regulatory networks is a major challenge.

RESULTS:

We present an algorithm that determines the smallest perturbations required for manipulating the dynamics of a network formulated as a Petri net, in order to cause or avoid a specified phenotype. By modifying McMillan's unfolding algorithm, we handle partial knowledge and reduce computation cost. The methodology is demonstrated on a glioma network. Out of the single gene perturbations, activation of glutathione S-transferase P (GSTP1) gene was by far the most effective in blocking the cancer phenotype. Among pairs of perturbations, NFkB and TGF-beta had the largest joint effect, in accordance with their role in the EMT process.

CONCLUSION:

Our method allows perturbation analysis of regulatory networks and can overcome incomplete information. It can help in identifying drug targets and in prioritizing perturbation experiments.

• Authors: Guy Karlebach, Ron Shamir
• Journal: BMC systems biology (BMC Syst Biol) Vol. 4 Pg. 15 (Feb 25 2010) ISSN: 1752-0509 [Electronic] England
• PMID: 20184733 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• Neoplasm Proteins

Topics

• Algorithms
• Animals
• Brain Neoplasms (genetics)
• Computer Simulation
• Gene Expression Regulation, Neoplastic (genetics)
• Genetic Engineering (methods)
• Genetic Predisposition to Disease (genetics)
• Glioma (genetics)
• Humans
• Models, Genetic
• Neoplasm Proteins (genetics)
• Phenotype
• Signal Transduction (genetics)