Neural Networks and Metabolic Networks: Fault Tolerance and Robustness Features

Abstract

The main objective of this work is the comparison between metabolic networks and neural networks (ANNs) in terms of their robustness and fault tolerance capabilities. In the context of metabolic networks errors are random removal of network nodes, while attacks are failures in the network caused intentionally. In the contest of neural networks errors are usually defined configurations of input submitted to the network that are affected by noise, while the failures are defined as the removal of some network neurons. This study have proven that ANNs are very robust networks, with respect to the presence of noise in the inputs, and the partial removal of some nodes, until it reached a critical threshold; while, metabolic networks are very tolerant to random failures (absence of a critical threshold), but extremely vulnerable to targeted attacks.