Abstract

Abstract: The Stochastic Simulation Algorithm (SSA) is a milestone in the realm of stochastic modeling of biological systems, as it inspires all the current algorithms for stochastic simulation. Essentially, the SSA shows that under certain hypothesis the time to the next occurrence of a biochemical reaction is a random variable following a negative exponential distribution. Unfortunately, the hypothesis underlying SSA are difficult to meet, and modelers have to face the impact of assuming exponentially distributed reactions besides the prescribed scope of applicability. An opportunity of investigation is offered by the use of generally distributed reaction times.

This talk describes how general distributions are introduced into BlenX, a programming language designed for specifying biological models. In particular, the concepts of causality and enabling memory discipline are adapted to BlenX. Then the new extension is used to investigate how the quantitative behavior of a model is affected by the choice of the reaction time distribution. In particular, a few biological examples of increasing complexity are presented and the associated simulations discussed.

Biography: Short bio Davide Prandi obtained his Master’s degree in Computer Science from the University of Verona (Italy) and his PhD in Information and Communication Technology from the University of Trento (Italy). He was a postdoctoral researcher at the University of Trento (Italy) and a researcher at the ‘Magna Graecia’ University of Catanzaro (Italy). Davide joined CoSBi in April 2008.