Chemical combination effects predict connectivity in biological systems

Alexis A Borisy,Adrian M Heilbut,Raymond A Molnar,Grant R Zimmermann,Brent R Stockwell,Margaret S Lee,Glenn F Short,Joseph Lehár,Curtis T Keith,Andrew S Krueger,Jebediah T Ledell,Leanne C Giusti,Garry P Nolan,Omar A Magid

doi:10.1038/msb4100116

Abstract

Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.

Highlights

Living organisms are built of interacting components, whose function and dysfunction can be described through dynamic network models (Davidson et al, 2002)
Systems Biology involves the iterative construction of such models (Ideker et al, 2001), and may eventually improve the understanding of diseases using in silico simulations
We considered four models for combination effect morphology (Figure 2 and Materials and methods) that reflect historical combination analyses and that represent many of the responses observed in our therapeutic combination screens

Summary

Introduction

Living organisms are built of interacting components, whose function and dysfunction can be described through dynamic network models (Davidson et al, 2002). Systems Biology involves the iterative construction of such models (Ideker et al, 2001), and may eventually improve the understanding of diseases using in silico simulations. Such simulations may eventually permit drugs to be prioritized for clinical trials, reducing potential risks and increasing the likelihood of successful outcomes. Chemical combinations show promise, and a proliferation experiment with yeast mutants in the presence of probe mixtures (Haggarty et al, 2003) has found that chemical profiles correlate with genetic similarity This potential is confirmed by recent experiments using antibacterial combinations (Yeh et al, 2006) that show a relationship between synergy and chemical target relatedness

Methods

Results

Conclusion