Ulster University Logo

Ulster Institutional Repository

eNelator: a simulation system for large-scale vulnerability analysis of species-, disease- and process-specific protein networks

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Zheng, Huiru, Wang, Haiying and Azuaje, Francisco (2010) eNelator: a simulation system for large-scale vulnerability analysis of species-, disease- and process-specific protein networks. Journal of Computational Science, 1 (4). pp. 197-205. [Journal article]

Full text not available from this repository.

URL: http://dx.doi.org/10.1016/j.jocs.2010.08.002

DOI: 10.1016/j.jocs.2010.08.002

Abstract

The identification of vulnerabilities in protein networks is a promising approach to predicting potential therapeutic targets. Different methods have been applied to domain-specific applications, with an emphasis on single-node deletions. There is a need to further assess significant associations between vulnerability, functional essentiality and topological features across species, processes and diseases. This requires the development of open, user-friendly systems to generate and test existing hypotheses about the vulnerability of networks in the face of dysfunctional components. We implemented methodologies to estimate the vulnerability of different networks to the dysfunction of different combinations of components, under random and directed attack scenarios. To demonstrate the relevance of our approaches and software, published protein-protein interaction (PPI) networks from S. cerevisiae, E. coli and H. sapiens were analyzed. A PPI network implicated in the development of human heart failure, and signaling networks relevant to Caspase3 and P53 regulation were also investigated. Known essential proteins (individually or in groups) have no detectable effects on network stability. Some of the most vulnerable proteins are neither essential nor hubs. Known diagnostic biomarkers have little effect on the communication efficiency of the disease network. Predictions made on the signaling networks are consistent with recent experimental evidence. Our system, which integrates other quantitative measures, can assist in the identification of potential drug targets and systems-level properties. The system for large-scale analysis of random and directed attacks is freely available, as a Cytoscape plugin, on request from the authors.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
Computer Science Research Institute > Smart Environments
ID Code:15813
Deposited By:Dr Huiru Zheng
Deposited On:06 Dec 2010 11:25
Last Modified:08 Dec 2010 12:36

Repository Staff Only: item control page