Abstract
An iterative approach that integrates high-throughput measurements of yeast deletion mutants and flux balance model predictions improves understanding of both experimental and computational results.
Highlights
Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology
We focused on 465 of the 892 genes present in one of the stoichiometric models, which are non-essential for growth in rich glucose medium (YPD) and for which a homozygous diploid deletion mutant was publicly available [10] (Table S1 in Additional data file 2)
By generating a yeast compendium of experimentally determined phenotypes for single gene deletion mutants of metabolic genes and predictions from stoichiometric models, we explored ways in which genome scale experimentation and modeling can be utilized synergistically
Summary
Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored. Genome-scale metabolic network stoichiometries, encompassing all known metabolic reactions for a given organism, have been published for a diverse set of organisms, ranging from Escherichia coli [1] to human [2]. These network stoichiometries have been used to build quantitative models capable of producing biologically informative and experimentally testable predictions [3,4]. The ability to determine growth phenotypes in a high-throughput manner, both experimentally [9,10] and in flux balance models, has contributed to making model comparisons to single deletion mutant growth phenotypes a community standard in the assessment of new models [11,12,13]
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