Abstract
Sampling the solution space of genome-scale models is generally conducted to determine the feasible region for metabolic flux distribution. Because the region for actual metabolic states resides only in a small fraction of the entire space, it is necessary to shrink the solution space to improve the predictive power of a model. A common strategy is to constrain models by integrating extra datasets such as high-throughput datasets and C13-labeled flux datasets. However, studies refining these approaches by performing a meta-analysis of massive experimental metabolic flux measurements, which are closely linked to cellular phenotypes, are limited. In the present study, experimentally identified metabolic flux data from 96 published reports were systematically reviewed. Several strong associations among metabolic flux phenotypes were observed. These phenotype-phenotype associations at the flux level were quantified and integrated into a Saccharomyces cerevisiae genome-scale model as extra physiological constraints. By sampling the shrunken solution space of the model, the metabolic flux fluctuation level, which is an intrinsic trait of metabolic reactions determined by the network, was estimated and utilized to explore its relationship to gene expression noise. Although no correlation was observed in all enzyme-coding genes, a relationship between metabolic flux fluctuation and expression noise of genes associated with enzyme-dosage sensitive reactions was detected, suggesting that the metabolic network plays a role in shaping gene expression noise. Such correlation was mainly attributed to the genes corresponding to non-essential reactions, rather than essential ones. This was at least partially, due to regulations underlying the flux phenotype-phenotype associations. Altogether, this study proposes a new approach in shrinking the solution space of a genome-scale model, of which sampling provides new insights into gene expression noise.
Highlights
As a powerful tool for biological interpretation and discovery, over 100 genome-scale metabolic networks for more than 35 organisms have been reconstructed
Because microbial metabolism is operated under certain global principles [24,25,26,27], we hypothesized that metabolic flux phenotypes are under synergistical regulations, and correlate with each other
By summarizing the metabolic data that have accumulated in the past decades, we have identified several metabolic flux phenotype correlations, which were subsequently and successfully applied to the refinement of solution space
Summary
As a powerful tool for biological interpretation and discovery, over 100 genome-scale metabolic networks for more than 35 organisms have been reconstructed (see http://gcrg.ucsd.edu/ InSilicoOrganisms/OtherOrganisms). Instead of developing a system of partial differential equations such as that observed in kinetic models, the constraint-based modeling method converts a metabolic network into a stoichiometric matrix [13]. In reality, the metabolic behavior of a cell is under complex physiological regulations, many of which have not been reflected by the constraints mentioned above This implies that the region for biologically relevant flux distribution is represented by small fraction of the entire solution space of a constraint-based model (CBM). Shrinking the predicted solution space is necessary to improve the predictive ability of a CBM
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