Perturbation Biology: Inferring Signaling Networks in Cellular Systems Journal Article

Authors: Molinelli, E. J.; Korkut, A.; Wang, W.; Miller, M. L.; Gauthier, N. P.; Jing, X.; Kaushik, P.; He, Q.; Mills, G.; Solit, D. B.; Pratilas, C. A.; Weigt, M.; Braunstein, A.; Pagnani, A.; Zecchina, R.; Sander, C.
Article Title: Perturbation Biology: Inferring Signaling Networks in Cellular Systems
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. © 2013 Molinelli et al.
Journal Title: PLoS Computational Biology
Volume: 9
Issue: 12
ISSN: 1553-7358
Publisher: Public Library of Science  
Date Published: 2013-12-01
Start Page: e1003290
Language: English
DOI: 10.1371/journal.pcbi.1003290
PROVIDER: scopus
PMCID: PMC3868523
PUBMED: 24367245
Notes: Export Date: 3 March 2014 -- Source: Scopus
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MSK Authors
  1. David Solit
    440 Solit
  2. Martin Lee Miller
    14 Miller
  3. Xiaohong Jing
    10 Jing
  4. Anil Korkut
    6 Korkut
  5. Wei-Qing Wang
    9 Wang
  6. Chris Sander
    196 Sander
  7. Qin He
    2 He