Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes

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Abstract

<title>Author Summary</title> <p>The sequencing of full genomes and the development of high-throughput analysis technologies have made available both genome-scale metabolic networks and simultaneous transcription data for all the genes of an organism. Genome-scale metabolic models, with the assumption of steady state for the internal metabolites, allow the definition of a region of feasible metabolic flux distributions. This space of solutions can be further constrained using experimental flux measurements (normally production or uptake rates of external compounds). Here a random sampling method was used to obtain average values and standard deviations for all the reaction rates in a genome-scale model. These values were used to quantify the significance of changes in metabolic fluxes between different conditions. The significance in flux changes can be compared to the changes in gene transcription of the corresponding enzymes. Our method allowed for identification of specific reactions that are transcriptionally regulated, and we further identified that these reactions can be ascribed to a few key transcription factors. This suggests that the regulation of metabolism has evolved to contain a few flux-regulating transcription factors that could be the target for genetic manipulations in order to redirect fluxes.</p>

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Bordel, S., Agren, R., & Nielsen, J. (2010). Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Computational Biology, 6(7), 16. https://doi.org/10.1371/journal.pcbi.1000859

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