Computational modeling of biochemical systems using cellular automata

Abstract

COMPUTATIONAL MODELING OF BIOCHEMICAL SYSTEMS USING CELLULAR AUTOMATA By Advait Ashok Apte, Ph.D. A Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at Virginia Commonwealth University. Virginia Commonwealth University, 2009 Major Director: Stephen S. Fong Assistant Professor, Chemical and Life Science Engineering Biological systems exhibit complex behaviors through coordinated responses of individual biological components. With the advent of genome-scale techniques, one focus has been to develop methods to model interactions between components to accurately describe intact system function. Mathematical modeling techniques such as constraintbased modeling, agent-based modeling, cellular automata (CA) modeling and differential equation modeling are employed as computational tools to study biological phenomenon. We have shown that cellular automata simulations can be used as a computational tool for 12 predicting the dynamics of biological systems with stochastic behavior. The basic premise for the research was the observations made during a study of biologically important feedforward motifs where CA simulations were compared with differential equation simulations. It was shown for classes of structural motifs with feed-forward architecture that network topology affects the overall rate of a process in a quantitatively predictable manner. The study which comprised of CA simulations compared with differential equation modeling show reasonable agreement in the predictability of system dynamics, which provided enough support to model biological systems at cellular level to observe dynamic system evolution. The great promise shown by CA simulations to model biochemical systems was then employed to elucidate evolutionary clues as to why biological networks show preference for certain types of motifs and preserve them with higher frequency during evolution. It was followed by modeling apoptotic networks to shed light on the efficacy of inhibitors and to model cellulose hydrolysis to evaluate efficiency of different enzyme systems used by cellulytic bacteria.