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Predicting Phenotype from Genotype Through Reconstruction and Integrative Modeling of Metabolic and Regulatory Networks

  • Sriram Chandrasekaran
Chapter

Abstract

A central challenge in systems biology is the identification of molecular interactions that regulate organismal phenotype, and to predict phenotypic changes that arise from these interacting networks. The reconstruction of gene networks provides a mechanistic basis for understanding the genotype-phenotype relationship, and enables the simulation of cellular behavior resulting from genetic and environmental perturbations. Currently, there is a critical need for new methods that rapidly transform high-throughput genomics, transcriptomics and metabolomics data into such predictive network models for metabolic engineering and synthetic biology. This chapter describes tools and technologies that address these key challenges, with a focus on the algorithms, PROM and ASTRIX, which perform complementary functions in mapping and modeling gene networks. The Analyzing Subsets of Transcriptional Regulators Influencing eXpression (ASTRIX) approach builds Transcriptional Regulatory Networks from gene expression data while the Probabilistic Regulation of Metabolism (PROM) algorithm integrates disparate gene networks (metabolic and regulatory networks) together in an automated fashion. Some basic principles of reconstructing and modeling these networks are discussed, followed by a detailed description of these algorithms. Understanding how the networks function together in a cell will pave the way for synthetic biology and has wide-ranging applications in biotechnology, drug discovery and diagnostics.

Keywords

Metabolic network Transcriptional regulatory network (TRN) Constraint-based modeling  Probabilistic regulation of metabolism (PROM)  Network inference  Analyzing subsets of transcriptional regulators influencing expression (ASTRIX) 

Notes

Acknowledgments

I acknowledge funding through an International Predoctoral Fellowship from the Howard Hughes Medical Institute; I thank Dr. Nathan Price for valuable guidance and James Eddy for help with making some of the figures.

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© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Institute for Systems BiologySeattleUSA
  2. 2.Center for Biophysics and Computational BiologyUniversity of Illinois at Urbana-ChampaignChampaignUSA

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