Comparative Interaction Networks: Bridging Genotype to Phenotype

Part of the Advances in Experimental Medicine and Biology book series (volume 751)

Abstract

Over the past decade, biomedical research has witnessed an exponential increase in the throughput of the characterization of biological systems. Here we review the recent progress in large-scale methods to determine protein–protein, genetic and chemical–genetic interaction networks. We discuss some of the limitations and advantages of the different methods and give examples of how these networks are being used to study the evolutionary process. Comparative studies have revealed that different types of protein–protein interactions diverge at different rates with high conservation of co-complex membership but rapid divergence of more promiscuous interactions like those that mediate post-translational modifications. These evolutionary trends have consistent genetic consequences with highly conserved epistatic interactions within complex subunits but faster divergence of epistatic interactions across complexes or pathways. Finally, we discuss how these evolutionary observations are being used to interpret cross-species chemical-genetic studies and how they might shape therapeutic strategies. Together, these interaction networks offer us an unprecedented level of detail into how genotypes are translated to phenotypes, and we envision that they will be increasingly useful in the interpretation of genetic and phenotypic variation occurring within populations as well as the rational design of combinatorial therapeutics.

Notes

Acknowledgments

We thank J. Haber for critically reading the manuscript and funding from the NIH (GM082250, GM084448, GM084279, AI090935, GM081879, AI091575, GM098101). NJK is a Searle Scholar and Keck Young Investigator. PB is supported by the Human Frontiers Science Program. CR is supported by IRCSET.

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© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical ResearchUniversity of California, San FranciscoSan FranciscoUSA
  2. 2.School of Computer Science and InformaticsUniversity College DublinDublinIreland
  3. 3.J. David Gladstone InstitutesSan FranciscoUSA

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