Models from experiments: combinatorial drug perturbations of cancer cells

Weiqing Wang,Peter Gennemark,Qing‐Bai She,Neal Rosen,Chris Sander,Sven Nelander,Christine Pratilas,Björn Nilsson

doi:10.1038/msb.2008.53

Abstract

We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built-in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho-proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.

Highlights

Our ability to measure increasingly complete and accurate molecular profiles of living cells motivates new quantitative approaches to cell biology
We have developed a particular approach to constructing, optimizing and applying computational models of cellular processes, which we call Combinatorial Perturbation-based Interaction Analysis (CoPIA)
State space models with multiple inputs–outputs are called multiple input–multiple output (MIMO) models

Summary

Introduction

Our ability to measure increasingly complete and accurate molecular profiles of living cells motivates new quantitative approaches to cell biology. A key aim of systems biology is to relate changes in molecular behavior to phenotypic consequences. To achieve this aim, computational models of cellular processes are extremely useful, if not essential. Computational models can be used for the analysis of experimental data, for the prediction of outcomes of unseen experiments and for planning interventions designed to modify system behavior. We have developed a particular approach to constructing, optimizing and applying computational models of cellular processes, which we call Combinatorial Perturbation-based Interaction Analysis (CoPIA).

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Conclusion