Abstract
Genome-scale metabolic models are frequently used in computational biology. They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. However, the huge solution space which comes with the analysis of genome-scale models by using, e.g., Flux Balance Analysis (FBA) poses a problem, since it is hard to thoroughly investigate and often only an arbitrarily selected individual flux distribution is discussed as an outcome of FBA. Here, we introduce a new approach to inspect the solution space and we compare it with other approaches, namely Flux Variability Analysis (FVA) and CoPE-FBA, using several different genome-scale models of lactic acid bacteria. We examine the extent to which different types of experimental data limit the solution space and how the robustness of the system increases as a result. We find that our new approach to inspect the solution space is a good complementary method that offers additional insights into the variance of biological phenotypes and can help to prevent wrong conclusions in the analysis of FBA results.
Highlights
Computational modelling has become a standard approach to achieve a comprehensive understanding of metabolic networks
To investigate the solution space, as well as the robustness of the solutions produced by flux balance analysis (FBA), a published genome-scale metabolic model of E. faecalis, as well as models of S. pyogenes [20] and L. lactis [21] were used
The robustness of the steady-state flux distribution as calculated by FBA was examined by extensive analysis of the solution space
Summary
Computational modelling has become a standard approach to achieve a comprehensive understanding of metabolic networks. In the absence of detailed kinetic data and in order to represent metabolism as a whole, genome-scale models based on the stoichiometric wiring of the system are a suitable way to, e.g., determine permissible and optimal flux distributions [1]. For this purpose, an optimality criterion, usually biomass production has to be defined [2] and optimal flux distributions are calculated. Transcriptome and proteome data have recently been used to reduce the solution space of the optimization. The abundancies of transcripts or proteins can be used to constrain the respective flux bounds [6,7,8]
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