[Article] Perturbation Biology: Inferring Signaling Networks in Cellular Systems

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. Citation: Molinelli EJ, Korkut A, Wang W, Miller ML, Gauthier NP, et al. (2013) Perturbation Biology: Inferring Signaling Networks in Cellular Systems. PLoS Comput Biol 9(12): e1003290. doi:10.1371/journal.pcbi.1003290 Editor: Florian Markowetz, Cancer Research UK Cambridge Research Institute, United Kingdom Received October 1, 2012; Accepted August 26, 2013; Published December 19, 2013 Copyright: 2013 Molinelli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The research was supported by a grant from the Integrative Cancer Biology Program (ICBP CCS U54CA148967), the Pathway Commons grant (NHGRIU41HG006623), the Melanoma Research Alliance (236191), and the National Resource for Network Biology grant (NIGMS-GM103504). EJM and PK were partially supported by a training grant from the Tri-Institutional Program for Computational Biology and Medicine (National Institute of General Medical Sciences T32GM083937). RZ was supported by the European Research Council (ERC) grant n. 267915 .The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: [email protected] . These authors contributed equally to this work.