Title: Integrative identification of core genetic regulatory modules via a structural model-based clustering method
Authors: Binhua Tang, Su-Shing Chen, Victor X. Jin
Addresses: Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA; College of Computer and Information, Hohai University, Jiangsu 213022, China. ' CISE and Systems Biology Lab, University of Florida, Gainesville, FL 32611, USA. ' Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
Abstract: Regulatory modules play fundamental roles in processing and dispatching signals in cell life cycle. Although current clustering methods may reduce data complexity to lower dimension, they tend to neglect biological meanings within high-throughput data. We propose a module-detection algorithm through defining network activity measures and associating them through a weighted clustering approach. We verify our method on diverse models and it provides a unique perspective for analysing model dynamics and expression data, especially with consideration of inherent biological meanings. As it can detect core regulatory modules effectively, it facilitates pathway/network modelling in systems biology.
Keywords: genetic regulatory modules; structural model clustering; dynamic modelling; gene expression data; systems biology; pathway models; network models.
International Journal of Computational Biology and Drug Design, 2011 Vol.4 No.2, pp.127 - 146
Published online: 24 Jan 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article