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.
Highlights
Recent comparative studies of genomic-scale gene expression levels have revealed substantial variation among different strains [1,2], among closely related species [3], and even within a genetically identical population [4,5,6,7]
Schuster et al and Famili et al have shown that genes associated with fluxes that are predicted to change together during a shift from one medium to another are coexpressed under these conditions, while Reed and Palsson have shown that the genes associated with fluxes that are correlated within the solution space exhibit moderate levels of correlation in their expression [12,26,27]
What do the metabolic flux distributions composing the Flux balance analysis (FBA) solution space represent? While some of them may be superfluous, arising from missing constraints, this study shows that as a whole, they are biologically meaningful
Summary
Recent comparative studies of genomic-scale gene expression levels have revealed substantial variation among different strains [1,2], among closely related species [3], and even within a genetically identical population [4,5,6,7]. A key tool in studying metabolic networks is constraintbased modeling, which permits analysis of large-scale networks. Accurate prediction of dynamic metabolic activity requires kinetic models, but these rely on detailed information of the rates of enzyme activity, which is mostly unavailable, and are limited to small-scale networks. Constraint-based models use genome-scale networks to predict steady-state metabolic activity, regardless of specific enzyme kinetics. Flux balance analysis (FBA) [8,9] is a specific, constraint-based method which assumes that the network is regulated to maximize or minimize a certain cellular function, which is usually taken to be the organism’s growth rate. FBA has been successfully used for predicting growth, uptake rates, byproduct secretion, and growth following adaptive evolution, as well as other phenotypes [10,11,12,13,14]
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