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Perturbation biology: inferring signaling networks in cellular systems.

TitlePerturbation biology: inferring signaling networks in cellular systems.
Publication TypeJournal Article
Year of Publication2013
AuthorsMolinelli, EJ, Korkut, A, Wang, W, Miller, ML, Gauthier, NP, Jing, X, Kaushik, P, He, Q, Mills, G, Solit, DB, Pratilas, CA, Weigt, M, Braunstein, A, Pagnani, A, Zecchina, R, Sander, C
JournalPLoS Comput Biol
Date Published2013
KeywordsCell Line, Tumor, Humans, Models, Biological, Monte Carlo Method, Probability, Signal Transduction, Systems Biology

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.

Alternate JournalPLoS Comput. Biol.
PubMed ID24367245
PubMed Central IDPMC3868523
Grant ListGM103504 / GM / NIGMS NIH HHS / United States
T32GM083937 / GM / NIGMS NIH HHS / United States
U41 HG006623 / HG / NHGRI NIH HHS / United States
U41HG006623 / HG / NHGRI NIH HHS / United States
U54 CA148967 / CA / NCI NIH HHS / United States
U54CA148967 / CA / NCI NIH HHS / United States

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