Comparative analysis of asynchronous cellular automata in stochastic pharmaceutical modelling

Abstract

In pharmaceutical modelling, cellular automata have been used as an established tool to represent molecular changes through discrete structural interactions. The data quality provided by such modelling is found suitable for the early drug design phase where flexibility is paramount. While both synchronous (CA) and asynchronous (ACA) types of automata have been used, analysis of their nature and comparative influence on model outputs is lacking. In this paper, we outline a representative probabilistic CA for modelling complex controlled drug formulations and investigate its transition from synchronous to asynchronous update algorithms. The key investigation points include quantification of model dynamics through three distinct scenarios, parallelisation performance and the ability to describe different release phenomena, namely erosion, diffusion and swelling. The choice of the appropriate update mechanism impacts the perceived realism of the simulation as well as the applicability of large-scale simulations.

A cooperative learning method based on cellular learning automata and its application in optimization problems

“…Despite the simple structure of CA, they are able to model non-linear dynamics in natural systems, which are very hard to model by other mathematical tools like differential equations [35,36,37,38]. Here, we employ CAs to facil- …”