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PLoS Comput Biol. 2013;9(12):e1003290. doi: 10.1371/journal.pcbi.1003290. Epub 2013 Dec 19.

Perturbation biology: inferring signaling networks in cellular systems.

Author information

1
Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America ; Tri-Institutional Program for Computational Biology and Medicine, Weill Cornell Medical College, New York, New York, United States of America.
2
Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
3
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
4
Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America ; Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
5
Program in Molecular Pharmacology, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America ; Department of Pediatrics, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.
6
Laboratoire de Génomique des Microorganismes, Université Pierre et Marie Curie, Paris, France.
7
Politecnico di Torino and Human Genetics Foundation, HuGeF, Torino, Italy.

Abstract

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.

PMID:
24367245
PMCID:
PMC3868523
DOI:
10.1371/journal.pcbi.1003290
[Indexed for MEDLINE]
Free PMC Article

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