Title: Multiscale Modeling of Metabolism and Macromolecular Synthesis in E. coli and Its Application to the Evolution of Codon Usage

Abstract

Biological systems are inherently hierarchal and multiscale in time and space. A major challenge of systems biology is to describe biological systems as a computational model, which can be used to derive novel hypothesis and drive experiments leading to new knowledge. The constraint-based reconstruction and analysis approach has been successfully applied to metabolism and to the macromolecular synthesis machinery assembly. Here, we present the first integrated stoichiometric multiscale model of metabolism and macromolecular synthesis for Escherichia coli K12 MG1655, which describes the sequence-specific synthesis and function of almost 2000 gene products at molecular detail. We added linear constraints, which couple enzyme synthesis and catalysis reactions. Comparison with experimental data showed improvement of growth phenotype prediction with the multiscale model over E. coli’s metabolic model alone. Many of the genes covered by this integrated model are well conserved across enterobacters and other, less related bacteria. We addressed the question of whether the bias in synonymous codon usage could affect the growth phenotype and environmental niches that an organism can occupy. We created two classes of in silico strains, one with more biased codon usage and one with more equilibrated codon usage than the wildtype. The reduced growth phenotype in biased strainsmore » was caused by tRNA supply shortage, indicating that expansion of tRNA gene content or tRNA codon recognition allow E. coli to respond to changes in codon usage bias. Our analysis suggests that in order to maximize growth and to adapt to new environmental niches, codon usage and tRNA content must co-evolve. These results provide further evidence for the mutation-selection-drift balance theory of codon usage bias. This integrated multiscale reconstruction successfully demonstrates that the constraintbased modeling approach is well suited to whole-cell modeling endeavors.« less

Authors:

Thiele, Ines ^[1]; Fleming, Ronan M. T. ^[2]; Que, Richard ^[3]; Bordbar, Aarash ^[3]; Diep, Dinh ^[3]; Palsson, Bernhard O. ^[3]

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+ Show Author Affiliations

1. University of Iceland, Reykjavik (Iceland); Univ. of California, San Diego, La Jolla, CA (United States)
2. University of Iceland, Reykjavik (Iceland)
3. Univ. of California, San Diego, La Jolla, CA (United States)

Publication Date:

Fri Sep 28 00:00:00 EDT 2012

Research Org.:

Stanford Univ., CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC); National Institutes of Health (NIH); European Commission (EC)

OSTI Identifier:

1904545

Grant/Contract Number:  

SC0002009; R0157089; Nr. 249261

Resource Type:

Accepted Manuscript

Journal Name:

PLoS ONE

Additional Journal Information:

Journal Volume: 7; Journal Issue: 9; Journal ID: ISSN 1932-6203

Publisher:

Public Library of Science

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; protein metabolism; transfer RNA; enzyme metabolism; metabolic networks; DNA transcription; messenger RNA; enterobacter; enzymes