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Mol Syst Biol. 2014 Jul 1;10:735. doi: 10.15252/msb.20145108.

An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli.

Author information

1
UC Davis Genome Center, University of California, Davis, CA, USA.
2
Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
3
UC Davis Genome Center, University of California, Davis, CA, USA Department of Computer Science, University of California, Davis, CA, USA.
4
UC Davis Genome Center, University of California, Davis, CA, USA Department of Computer Science, University of California, Davis, CA, USA iliast@ucdavis.edu.

Abstract

Given the vast behavioral repertoire and biological complexity of even the simplest organisms, accurately predicting phenotypes in novel environments and unveiling their biological organization is a challenging endeavor. Here, we present an integrative modeling methodology that unifies under a common framework the various biological processes and their interactions across multiple layers. We trained this methodology on an extensive normalized compendium for the gram-negative bacterium Escherichia coli, which incorporates gene expression data for genetic and environmental perturbations, transcriptional regulation, signal transduction, and metabolic pathways, as well as growth measurements. Comparison with measured growth and high-throughput data demonstrates the enhanced ability of the integrative model to predict phenotypic outcomes in various environmental and genetic conditions, even in cases where their underlying functions are under-represented in the training set. This work paves the way toward integrative techniques that extract knowledge from a variety of biological data to achieve more than the sum of their parts in the context of prediction, analysis, and redesign of biological systems.

KEYWORDS:

genome engineering; genome‐scale model; model‐driven experimentation; predictive modeling and integration; systems and synthetic biology

PMID:
24987114
PMCID:
PMC4299492
DOI:
10.15252/msb.20145108
[Indexed for MEDLINE]
Free PMC Article

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