QSB: A Principled Mapping of Regulatory Networks to Asynchronous Circuit Models for Stochastic Analysis

University Of Utah, Salt Lake City UT

Investigators

Abstract

Microarrays and other new technologies are now giving us vast amounts of data on how genes interact to perform complex biological functions. In order to reason about genetic systems, a systems biology perspective must be taken in which new models and efficient analysis methods must be developed. Electrical engineers have vast experience modeling and analyzing electronic circuits and systems. McAdams and Shapiro in their 1995 Science paper took an electronic circuit view of a genetic network with encouraging results. Therefore, as in the sequencing of the human genome, collaborations between engineers and systems biologists may be extremely beneficial to the success of functional genomics. This grant will fund continuation of a new collaboration between the PI and Professor Adam Arkin's Laboratory for Dynamical Genomics at UC Berkeley. The PI has modeled the Phage_ virus using a stochastic asynchronous circuit model. A stochastic model appears to be essential as the survival strategy taken by this virus has a random component which may be key in the evolutionary survival of this and other species. Dr. Arkin's original model based on the chemical master equation and Monte Carlo simulation required substantial runtime on a supercomputer while the new stochastic asynchronous circuit model produces comparable results in under a minute on a PC. The Phage_ case study has led to the development of an abstraction methodology from reactions with kinetic rates and critical concentrations to a stochastic asynchronous circuit model. After this abstraction, efficient Markov chain analysis methods can be applied to reason about the systems behavior. The first major goal of this work will be to apply this methodology to other systems that exhibit stochastic behavior, such as the E. Coli Fim system or the B. Subtilis stress response network. The goal is a complete methodology for the efficient analysis of genetic regulatory networks and its demonstration on several example systems. All models and tools developed in the course of this research will be made available via the web for research and teaching at other institutions. The efficient analysis of biological systems in silico has the promise of helping our understanding of the causes of disease and our development of drugs to treat them.