Speaker(s):
Dieter Armbruster, Department of Mathematics and Statistics
Michael Lamarca, Department of Mathematics and Statistics
John Nagy, Scottsdale Community College

Title:Modeling Glycolysis as a Stochastic Factory

Abstract: Most biochemical and genetic reaction pathways involve a large number of intermediate species and complex nonlinear feedbacks between downstream products and upstream effector enzymes. Therefore, the dynamical system approaches typically used to model such pathways include high dimensional systems of nonlinear differential equations. These ODE models are usually based on an explicit assumption that the system contains large numbers of each species. However, this assumption fails in the intracellular environment, where metabolic systems are compartmentalized and tend to contain so few molecules that stochastic fluctuations can become a dominant feature of the dynamics. We present here a new simulation tool based on an analogy between factory production and production in a biochemical pathway that allows us to effectively generate large scale stochastic simulations. The tool is based on the idea of Discrete Event Simulations of stochastic processes. The method is illustrated for a simulation model of yeast glycolysis, treating glucose as a raw product, alcohol as the final product of the factory, the intermediate enzymes as machines and their production rates as the means of probability distributions. We show different dynamical behavior depending on ATP, NADH, the glucose arrival rate and the assumptions about the rate distributions and discuss their biological significance.