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, Be

SUBMITTER: Molinelli EJ 

PROVIDER: S-EPMC3868523 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature