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PMID: 16933982 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't

Conservation of expression and sequence of metabolic genes is reflected by activity across metabolic states.

PLoS computational biology ·Vol. 2 ·No. 8 ·2006-08-18 ·Pages e106

Bilu Y, Shlomi T, Barkai N, Ruppin E

Abstract

Variation in gene expression levels on a genomic scale has been detected among different strains, among closely related species, and within populations of genetically identical cells. What are the driving forces that lead to expression divergence in some genes and conserved expression in others? Here we employ flux balance analysis to address this question for metabolic genes. We consider the genome-scale metabolic model of Saccharomyces cerevisiae, and its entire space of optimal and near-optimal flux distributions. We show that this space reveals underlying evolutionary constraints on expression regulation, as well as on the conservation of the underlying gene sequences. Genes that have a high range of optimal flux levels tend to display divergent expression levels among different yeast strains and species. This suggests that gene regulation has diverged in those parts of the metabolic network that are less constrained. In addition, we show that genes that are active in a large fraction of the space of optimal solutions tend to have conserved sequences. This supports the possibility that there is less selective pressure to maintain genes that are relevant for only a small number of metabolic states.

MeSH Terms
Cell Proliferation Computer Simulation Gene Expression Regulation, Fungal/physiology Models, Biological Protein Interaction Mapping/methods Saccharomyces cerevisiae/physiology Saccharomyces cerevisiae Proteins/physiology Signal Transduction/physiology
Chemicals
Saccharomyces cerevisiae Proteins
Authors & Affiliations
4 authors, click to expand affiliations / ORCID
Bilu Yonatan
Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel. [email protected]
Shlomi Tomer
Barkai Naama
Ruppin Eytan
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Article Info
Journal
PLoS computational biology
Abbr.
PLoS Comput Biol
ISSN
1553-7358
Published
2006-08-18
Epub
2006-00-06
Pages
e106
Language
English
Region
United States
NLM ID
101238922
PMCID
PMC1550272
Subset
IM
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