Authors: Jingwei Zhang, Layne T. Watson, Yang Cao
Addresses: Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0106, USA. ' Departments of Computer Science and Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0106, USA. ' Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061-0106, USA
Abstract: One important aspect of biological systems such as gene regulatory networks and protein-protein interaction networks is the stochastic nature of interactions between chemical species. Such stochastic behaviour can be accurately modelled by the Chemical Master Equation (CME). However, the CME usually imposes intensive computational requirements when used to characterise molecular biological systems. The major challenge comes from the curse of dimensionality, which has been tackled by a few research papers. The essential goal is to aggregate the system efficiently with limited approximation errors. This paper presents an adaptive way to implement the aggregation process using information collected from Monte Carlo simulations. Numerical results show the effectiveness of the proposed algorithm.
Keywords: CME; chemical master equation; Markov chain; aggregation method; Monte Carlo simulation; systems biology; adaptive aggregation; molecular biology; gene regulatory networks; protein-protein interaction; stochastic modelling.
International Journal of Computational Biology and Drug Design, 2009 Vol.2 No.2, pp.134 - 148
Available online: 03 Oct 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article