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].
Note that the metabolic networks and random networks are separated when k is low, suggesting that metabolic networks have relatively higher modular level than random networks.
Metabolic networks have received significant attention in the past decade.
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.
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).
Gene essentiality discrepanices caused by metabolic network differences, such as additional reactions and/or isozymes, provide interesting insights.
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.
Many of these are scope gaps, caused by the limited scope of the metabolic network, and these have previously been identified [ 8, 10].
Some of these mutations could negatively affect the metabolic network of the cell, thus causing slower growth.
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.
