TY - JOUR
T1 - Minimally perturbing a gene regulatory network to avoid a disease phenotype
T2 - The glioma network as a test case
AU - Karlebach, Guy
AU - Shamir, Ron
N1 - Funding Information:
We thank Yoel Kloog and Marcelo Ehrlich for helpful discussions and comments. This study was supported in part by the European Community’s Seventh Framework Programme under grant agreement n° HEALTH-F4-2007-200767 for the APO-SYS project. GK is supported by a fellowship from the Edmond J. Safra Bioinformatics Program at Tel Aviv University.
PY - 2010/2/5
Y1 - 2010/2/5
N2 - 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-β 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.
AB - 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-β 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.
UR - http://www.scopus.com/inward/record.url?scp=77951894568&partnerID=8YFLogxK
U2 - 10.1186/1752-0509-4-15
DO - 10.1186/1752-0509-4-15
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AN - SCOPUS:77951894568
SN - 1752-0509
VL - 4
JO - BMC Systems Biology
JF - BMC Systems Biology
M1 - 15
ER -