TY - JOUR T1 - Perturbation Biology: Inferring Signaling Networks in Cellular Systems A1 - Molinelli, Evan J. A1 - Korkut, Anil A1 - Wang, Weiqing A1 - Miller, Martin L. A1 - Gauthier, Nicholas P. A1 - Jing, Xiaohong A1 - Kaushik, Poorvi A1 - He, Qin A1 - Mills, Gordon A1 - Solit, David B. A1 - Pratilas, Christine A. A1 - Weigt, Martin A1 - Braunstein, Alfredo A1 - Pagnani, Andrea A1 - Zecchina, Riccardo A1 - Sander, Chris Y1 - 2013/12/19 N2 - Author Summary Drugs that target specific effects of signaling proteins are promising agents for treating cancer. One of the many obstacles facing optimal drug design is inadequate quantitative understanding of the coordinated interactions between signaling proteins. De novo model inference of network or pathway models refers to the algorithmic construction of mathematical predictive models from experimental data without dependence on prior knowledge. De novo inference is difficult because of the prohibitively large number of possible sets of interactions that may or may not be consistent with observations. Our new method overcomes this difficulty by adapting a method from statistical physics, called Belief Propagation, which first calculates probabilistically the most likely interactions in the vast space of all possible solutions, then derives a set of individual, highly probable solutions in the form of executable models. In this paper, we test this method on artificial data and then apply it to model signaling pathways in a BRAF-mutant melanoma cancer cell line based on a large set of rich output measurements from a systematic set of perturbation experiments using drug combinations. Our results are in agreement with established biological knowledge, predict novel interactions, and predict efficacious drug targets that are specific to the experimental cell line and potentially to related tumors. The method has the potential, with sufficient systematic perturbation data, to model, de novo and quantitatively, the effects of hundreds of proteins on cellular responses, on a scale that is currently unreachable in diverse areas of cell biology. In a disease context, the method is applicable to the computational design of novel combination drug treatments. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 9 IS - 12 UR - https://doi.org/10.1371/journal.pcbi.1003290 SP - e1003290 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1003290 ER -