Implementing a space-aware stochastic simulator on low-power architectures: a systems biology case study

In the last decade, different computing paradigms and modelling frameworks for the description and simulation of biochemical systems based on stochastic modelling have been proposed. From a computational point of view, many simulations of the model are necessary to identify the behaviour of the system. The execution of thousands of simulations can require huge amount of time, therefore the parallelization of these algorithms is highly desirable. In particular, models that consider the size of volumes and objects involved in the reaction are very timeconsuming, since many rules should be considered to take into account the position of the different molecules. In this work we present an implementation of a stochastic space-aware simulator which exploits the benefit and features of hybrid low-power computing architectures. This work shows that the simulator dynamic probabilistic approach to select possible chemical reactions can be applied and implemented in hybrid low-power low-cost architectures as well as current industry highend servers.