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Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks

Overview of attention for article published in PLoS Computational Biology, December 2009
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Title
Evolutionary Plasticity and Innovations in Complex Metabolic Reaction Networks
Published in
PLoS Computational Biology, December 2009
DOI 10.1371/journal.pcbi.1000613
Pubmed ID
Authors

João F. Matias Rodrigues, Andreas Wagner

Abstract

Genome-scale metabolic networks are highly robust to the elimination of enzyme-coding genes. Their structure can evolve rapidly through mutations that eliminate such genes and through horizontal gene transfer that adds new enzyme-coding genes. Using flux balance analysis we study a vast space of metabolic network genotypes and their relationship to metabolic phenotypes, the ability to sustain life in an environment defined by an available spectrum of carbon sources. Two such networks typically differ in most of their reactions and have few essential reactions in common. Our observations suggest that the robustness of the Escherichia coli metabolic network to mutations is typical of networks with the same phenotype. We also demonstrate that networks with the same phenotype form large sets that can be traversed through single mutations, and that single mutations of different genotypes with the same phenotype can yield very different novel phenotypes. This means that the evolutionary plasticity and robustness of metabolic networks facilitates the evolution of new metabolic abilities. Our approach has broad implications for the evolution of metabolic networks, for our understanding of mutational robustness, for the design of antimetabolic drugs, and for metabolic engineering.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 12 5%
Germany 4 2%
Spain 3 1%
United Kingdom 3 1%
Canada 3 1%
Japan 2 <1%
India 2 <1%
Switzerland 2 <1%
Netherlands 2 <1%
Other 7 3%
Unknown 203 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 63 26%
Researcher 55 23%
Student > Bachelor 27 11%
Student > Master 21 9%
Professor > Associate Professor 20 8%
Other 37 15%
Unknown 20 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 125 51%
Biochemistry, Genetics and Molecular Biology 28 12%
Computer Science 14 6%
Physics and Astronomy 14 6%
Mathematics 5 2%
Other 27 11%
Unknown 30 12%