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 eleme
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Abstract. 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. Keywords: Discrete systems · Controlled drug delivery systems Complex modelling · Parallel algorithms
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Introduction
Probabilistic models based on Monte Carlo and CA frameworks have emerged in recent years as a viable response to the modelling needs imposed by design requirements of novel, more complex, drug delivery systems (DDS) [1]. Unlike traditional, differential equation based models, [2,3], CA attempt to recreate system-level behaviour by in silico simulations of individual interactions within the modelled device. This fits naturally with the early stages of the design process, in which global physico-chemical behaviours of DDS are investigated. By providing a low-cost alternative to lengthy, and potentially expensive, in vitro experiments, probabilistic computational modelling becomes an integral part of the drug design process. Nevertheless, uncertainties are inherent in this approach to modelling physical phenomena and parameters can multiply rapidly, due to R. Wyrzykowski et al. (Eds.): PPAM 2013, Part II, LNCS 8385, pp. 699–710, 2014. c Springer-Verlag Berlin Heidelberg 2014 DOI: 10.1007/978-3-642-55195-6 66,
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the many different physical interactions within the model, so good understanding of model design choices is crucial. In research papers covering the application of CA to the field, both synchronous and asynchronous update methods have been used [3–6], without going into deeper analysis of pros and cons of each. Choosing the algorithm for iterating through the cellular matrix and the order of application of the local rules affects how temporal realism of the physical process is represented. As DDS is biological in nature, chaotic or random updates might represent the system dynamics better than synchronous, “all-at-once”, changes. On the other hand, as size and complexity of the models grows, the need for efficient parallelisation of model space restricts the application of the asynchronous methods due to performance reas
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