Stability Analysis of Biological Networks' Diffusion State.

Computational knowledge acquired from noisy networks is not reliable and the network topology determines the reliability. Protein-protein interaction networks have uncertain topologies and noise that contain false positive and false negative edges at high rates. In this study, we analyze effects of the existing mutations in a network topology to the diffusion state of that network. To evaluate the sensitivity of the diffusion state, we derive the fitness measures based on the mathematically defined stability of a network. Searching for an influential set of edges in a network is a difficult problem. We handle the computational challenge by developing a novel metaheuristic optimization method and we find influential mutations time-efficiently. Our experiments, conducted on both synthetic and real networks from public databases, demonstrated that our method obtained better results than competitors for all types of network topologies. This is the first-time that the diffusion has been evaluated under topological mutations. Our analysis identifies significant biological results about the stability of biological - synthetic networks and diffusion state. In this manner, mutations in protein-protein interaction network topologies have a significant influence on the diffusion state of the network. Network stability is more affected by the network model than the network size.