and different timescales. The utility of this simulation framework is demonstrated further with a ‘composite ’ heat-shock response model that combines the Gillespie–Gibson stochastic algorithm and deterministic differential equations. Dramatic improvements in performance were obtained without significant

and different timescales. The utility of this simulation framework is demonstrated further with a ‘composite ’ heat-shock response model that combines the Gillespie–Gibson stochastic algorithm and deterministic differential equations. Dramatic improvements in performance were obtained without significant

or where key reactions occur at a low rate. The Gille-spie stochastic simulation algorithm (SSA) is a procedure for generating time-evolution trajectories of finite populations in continuous time and has become the standard al-gorithm for these types of stochastic models. This article presents a simple

Abstract: Recent experimental studies elucidating the importance of noise in gene regulation have ignited widespread interest in Gillespie's stochastic simulation technique for biochemical networks. We formulate modifications to the Gillespie algorithm which are necessary to correctly

Abstract. We introduce a probabilistic algorithm for the simulation of chemical reactions, which can be used as an alternative to the wellestablished stochastic algorithm proposed by D.T. Gillespie in the ’70s. We show that the probabilistic evolution of systems derived by means of our algorithm

Mathematical/statistical modeling of biological systems is a desired goal for many years. It aims to be able to accurately predict the operation of such systems under various scenarios using computer simulations. In this paper we revisit Gille-spie’s Stochastic Simulation Algorithm for biochemical

Abstract not found

and Dissertations by an authorized administrator of VCU Scholars Compass. For more information, please contact libcompass@vcu.edu.

There is a plethora of ways to model biological systems, depending on size, detail required and questions asked. One method consists in writing down a collection of coupled ordinary differen-tial chemical equations, where each equation describes a number of reactions. The variables are the time dependent concentrations of participating molecules, and the parameters are reaction

-known discrete stochastic approaches is the computationally demanding Gillespie stochastic simulation algorithm which is in this work compared to the Itô stochastic differential equations. First, neuronal signal transduction is simulated using two different types of Itô stochastic differential equations