Probabilistic Pharmaceutical Modelling: A Comparison Between Synchronous and Asynchronous Cellular Automata

The field of pharmaceutical modelling has, in recent years, benefited from using probabilistic methods based on cellular automata, which seek to overcome some of the limitations of differential equation based models. By modelling discrete structural element interactions instead, these are able to provide data quality adequate for the early design phases in drug modelling. In relevant literature, both synchronous (CA) and asynchronous (ACA) types of automata have been used, without analysing their comparative impact on the model outputs. In this paper, we compare several variations of probabilistic CA and ACA update algorithms for building models of complex systems used in controlled drug delivery, analysing the advantages and disadvantages related to different modelling scenarios. Choosing the appropriate update mechanism, besides having an impact on the perceived realism of the simulation, also has practical benefits on the applicability of different model parallelisation algorithms and their performance when used in large-scale simulation contexts.