Abstract

Abstract: As more and more critical biological data become available and as the biological questions being addressed become more and more complex and sophisticated, the complexity to analyze the systems of interests becomes so high that tackling such a problem only with wet-lab experiments eventually becomes infeasible. Since /in silico/ experiments offer virtually unlimited controllabilities and observabilities of biochemical systems, computational methods can be an effective tool to shed some light on dynamics of biological systems under various conditions. Thus, integration of computational methods with the process of biological research becomes more imminent. However, this growing wealth of knowledge about biological processes has also led to the demand for progressively more sophisticated computational models, and as a result, although detailed, elementary-reaction level models can be constructed for a number of experimentally observed systems, their effectiveness is typically limited because of substantial runtime requirements caused by the multi timescale characteristics nature of many biological systems. This computational problem becomes more pronounced with the emerging understanding of the ubiquitous role played by nonlinear and discrete-stochastic molecular dynamics in gene regulatory, signal transduction, and other biological systems. One powerful tool to alleviate such complex problems is model abstraction based on a biological property of interest. In this talk, we will discuss the automated model abstraction methodology that we have developed for multi timescale biological systems. In particular, we will focus on application of this methodology to gene regulatory networks.

Biography: Hiroyuki Kuwahara obtained his PhD in Computer Science from the University of Utah (USA) in 2007. His thesis describes systematic and automatic model abstraction methodology to efficiently estimate temporal behaviors of genetic regulatory networks. Hiro joined CoSBi in September 2007.