Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 558975
Title Use of genome-scale metabolic models in evolutionary systems biology
Author(s) Papp, Balázs; Szappanos, Balázs; Notebaart, Richard A.
Source In: Yeast Systems Biology / Castrillo, Juan I., Oliver, Stephen G., Humana Press (Methods in Molecular Biology ) - ISBN 9781617791727 - p. 483 - 497.
DOI https://doi.org/10.1007/978-1-61779-173-4_27
Publication type Peer reviewed book chapter
Publication year 2011
Keyword(s) constraint-based modeling - fitness landscape - Flux balance analysis (FBA) - gene essentiality - genetic interaction - genome evolution - metabolic network - Saccharomyces cerevisiae
Abstract

One of the major aims of the nascent field of evolutionary systems biology is to test evolutionary hypotheses that are not only realistic from a population genetic point of view but also detailed in terms of molecular biology mechanisms. By providing a mapping between genotype and phenotype for hundreds of genes, genome-scale systems biology models of metabolic networks have already provided valuable insights into the evolution of metabolic gene contents and phenotypes of yeast and other microbial species. Here we review the recent use of these computational models to predict the fitness effect of mutations, genetic interactions, evolutionary outcomes, and to decipher the mechanisms of mutational robustness. While these studies have demonstrated that even simplified models of biochemical reaction networks can be highly informative for evolutionary analyses, they have also revealed the weakness of this modeling framework to quantitatively predict mutational effects, a challenge that needs to be addressed for future progress in evolutionary systems biology.

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