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

Guy Karlebach, Ron Shamir
<span title="">2010</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xua5vbjwszdirewaoqnikiu5zm" style="color: black;">BMC Systems Biology</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1752-0509-4-15">doi:10.1186/1752-0509-4-15</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/20184733">pmid:20184733</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC2851584/">pmcid:PMC2851584</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mfpakf2wr5cw5cbsb646oihfpm">fatcat:mfpakf2wr5cw5cbsb646oihfpm</a> </span>
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