TY - JOUR
T1 - Perturbation Biology
T2 - Inferring Signaling Networks in Cellular Systems
AU - Molinelli, Evan J.
AU - Korkut, Anil
AU - Wang, Weiqing
AU - Miller, Martin L.
AU - Gauthier, Nicholas P.
AU - Jing, Xiaohong
AU - Kaushik, Poorvi
AU - He, Qin
AU - Mills, Gordon
AU - Solit, David B.
AU - Pratilas, Christine A.
AU - Weigt, Martin
AU - Braunstein, Alfredo
AU - Pagnani, Andrea
AU - Zecchina, Riccardo
AU - Sander, Chris
PY - 2013/12
Y1 - 2013/12
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1003290
DO - 10.1371/journal.pcbi.1003290
M3 - Article
C2 - 24367245
AN - SCOPUS:84892763562
SN - 1553-734X
VL - 9
JO - PLoS computational biology
JF - PLoS computational biology
IS - 12
M1 - e1003290
ER -