In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random node removal and high-connectivity-degree node removal aimed at affecting network activities. The paper proposes a software framework, namely CEllDataLaB, based on three techniques to perform the structural and functional analysis of a metabolic network: topological analysis, flux balance analysis and extreme pathways analysis. The degradation of proteins into aminoacids metabolic network has been used to validate the implemented investigations strategies. The performed trials have shown that the node connectivity degrees as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network behavior and activities.
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