Perturbation Biology: inferring signaling networks in cellular systems

TL;DR

This paper introduces a new experimental-computational method called perturbation biology for inferring signaling networks in cancer cells, enabling prediction of cellular responses to drug perturbations and aiding in therapy design.

Contribution

The paper presents a novel approach combining systematic perturbation experiments with probabilistic computational modeling to infer signaling networks without prior pathway knowledge.

Findings

Successfully derived executable network models for melanoma cell lines

Models predict responses to drug combinations and suggest new interactions

Experimental validation confirms model predictions

Abstract

We present a new experimental-computational technology of 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 measured in terms of levels of proteins and phospho-proteins and of cellular phenotype 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, which is three orders of magnitude more efficient than 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 inhibition of Raf kinase. The resulting network models reproduce and extend known pathway biology. They can be applied to discover new molecular interactions and to predict the effect of novel drug perturbations, one of which is verified experimentally. The technology is suitable for application to larger systems in diverse areas of molecular biology.