The main objective of this work is to analyze metabolic networks evolution in terms of their robustness and fault tolerance capabilities. In metabolic networks, errors can be seen as random removal of network nodes, while attacks are high-connectivity-degree node deletion aimed at compromising network activity. This paper proposes a software framework, namely BioAnalysis, used to test the robustness and the fault tolerance capabilities of real metabolic networks, when mutations and node deletions affect the network structure. The performed simulations are related to the central metabolic network of the well-known E. coli single-celled bacterium and involve either hub nodes or non-hub nodes, whose influence on the network robustness and activity is different.The performed trials have shown that the node connectivity degree as well as the node functional role in the network are key issues to evaluate the impact of node deletion on network robustness and activity. With more details, functional analysis has demonstrated that low-connectivity-degree nodes may drastically influence the normal behaviour of the network, while high-connectivity-degree nodes may produce soft failure in network operations. The results coming from described simulations have been confirmed by similar in vivo laboratory tests on real cluster of E. Coli bacteria.
|Numero di pagine||8|
|Stato di pubblicazione||Published - 2010|
All Science Journal Classification (ASJC) codes
- Artificial Intelligence