Development and Analysis of Discrete Models of Cell Signaling Networks

August 30, 2011
10:30a-11:30a
Halligan 111a

Abstract

In medical research it is of great importance to be able to quickly obtain answers to inquiries about system responses to different stimuli. Modeling the dynamics of biological regulatory networks is a promising approach to achieve this goal, but existing modeling approaches suffer from complexity issues and become inefficient with large networks. Several recent works presented discrete, logical models and showed the benefits of applying logical approach to studying the dynamics of biological networks.

We propose a methodology for automating the development of such discrete models by utilizing methods and algorithms from the field of electronic design automation. To improve the efficiency of studying these models, we created a framework for model implementation in hardware, which allows for highly parallel model simulation. We find that our FPGA implementation of a model of peripheral na´ve T cell differentiation provides five orders of magnitude speedup when compared to software simulation. We anticipate that the proposed automated discrete model development and the speedup that our model implementation provides will greatly improve the efficiency of qualitative analysis of biological networks.

Bio: Natasa Miskov-Zivanov is a Research Associate in the Department of Computational and Systems Biology in the School of Medicine at the University of Pittsburgh and a Special Faculty at the Department of Electrical and Computer Engineering at Carnegie Mellon University. She received her Ph.D. and M.S. degrees in Electrical and Computer Engineering at Carnegie Mellon University, in 2008 and 2005, respectively, and a B.S. degree in Electrical Engineering and Computer Science from the University of Novi Sad, Serbia, in 2003. Her research interests include applications of computational methods in modeling and analysis of biological systems, emerging technologies and fault-tolerance in nanoscale designs.