Structural Systems Biology Evaluation of Metabolic Thermotolerance in Escherichia coli

See allHide authors and affiliations

Science  07 Jun 2013:
Vol. 340, Issue 6137, pp. 1220-1223
DOI: 10.1126/science.1234012

You are currently viewing the abstract.

View Full Text

Log in to view the full text

Log in through your institution

Log in through your institution

Improve the System

A "systems biology" approach may clarify, for example, how particular proteins determine sensitivity of bacteria to extremes of temperature. Chang et al. (p. 1220) integrated information on protein structure with a model of metabolism, thus associating the protein structure of enzymes with their catalyzed metabolic reactions. The effects of temperature on susceptible proteins could be predicted and the key reactions that were likely to mediate sensitivity of bacteria to extremes of temperature were identified. Indeed, engineered thermotolerant proteins could be substituted for sensitive ones to improve the growth of thermosensitive strains of bacteria. Such control could come in handy when engineering strains of bacteria to produce compounds of industrial or therapeutic value.


Genome-scale network reconstruction has enabled predictive modeling of metabolism for many systems. Traditionally, protein structural information has not been represented in such reconstructions. Expansion of a genome-scale model of Escherichia coli metabolism by including experimental and predicted protein structures enabled the analysis of protein thermostability in a network context. This analysis allowed the prediction of protein activities that limit network function at superoptimal temperatures and mechanistic interpretations of mutations found in strains adapted to heat. Predicted growth-limiting factors for thermotolerance were validated through nutrient supplementation experiments and defined metabolic sensitivities to heat stress, providing evidence that metabolic enzyme thermostability is rate-limiting at superoptimal temperatures. Inclusion of structural information expanded the content and predictive capability of genome-scale metabolic networks that enable structural systems biology of metabolism.

View Full Text