Exploiting non-Markovian Bio-Processes

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. In this paper, we describe how general distributions are introduced into BlenX, a programming language designed for specifying biological models. We then experiment the new extension on few examples of increasing complexity and discuss how the quantitative behaviour of a model is affected by the choice of the reaction time distribution.