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The presence of alternative pathways in a metabolic network causes FBA to predict a space of feasible flux distributions with a maximal growth rate, rather than a single solution [56].
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These mapping results indicate disrupted flows, accumulated metabolites, and differential transcription in the metabolic network caused by flooding stress (Komatsu et al. 2013a).
For example, a difference in the coverage of the metabolic network could be caused by a fundamental disagreement on whether certain processes are part of the human metabolic network.
Gene essentiality discrepanices caused by metabolic network differences, such as additional reactions and/or isozymes, provide interesting insights.
Some of these mutations could negatively affect the metabolic network of the cell, thus causing slower growth.
Alternatively, Klamt and Gilles (2004) proposed a model of minimal cut set (MCS) as a minimal set of reactions in metabolic networks whose disturbances cause dysfunction.
External perturbations arise due to changing environmental conditions, such as stress conditions (environmental perturbations), but also by changes in the structure of a metabolic networks (e.g., caused by gene knock-outs, also known as genetic perturbations).
In silico knock-out analysis provides an efficient method to study the essentiality of a reaction in a metabolic network and to gain insight into metabolic changes caused by the deletion.
We use these analyses to suggest how the metabolic network of the species has evolved and how these differences caused the divergent phenotypes.
Many of these are scope gaps, caused by the limited scope of the metabolic network, and these have previously been identified [ 8, 10].
In contrast, multi-function enzymes can potentially erroneously link two separate parts of metabolism and cause inferred modules to consist of otherwise disconnected pathways within the metabolic network.
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