![]() The presence of ample alternative solutions, however, limits the predictability of genome-scale metabolic models. The result is usually one specific flux distribution which, however, is by no means unique. This process which uses linear programming is called flux balance analysis (FBA). For this purpose, an optimality criterion, usually biomass production has to be defined and optimal flux distributions are calculated. 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. 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.Ĭomputational modelling has become a standard approach to achieve a comprehensive understanding of metabolic networks. 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. ![]() 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. 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. ![]() They offer an integrative view on the metabolic network of an organism without the need to know kinetic information in detail. ![]() Genome-scale metabolic models are frequently used in computational biology. ![]()
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