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

  • Marija Bezbradica
  • Heather J. Ruskin
  • Martin Crane
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8385)


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.


Discrete systems Controlled drug delivery systems Complex modelling Parallel algorithms 



Financial support from the ERA-Net Complexity Project, P07217, is warmly acknowledged. The parallel simulations discussed in this paper were performed on computational resources provided by Sci-Sym, DCU, and by the Irish Centre for High-End Computing.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Marija Bezbradica
    • 1
  • Heather J. Ruskin
    • 1
  • Martin Crane
    • 1
  1. 1.School of Computing, Centre for Scientific Research and Complex Systems Modelling (Sci-Sym)Dublin City UniversityDublinIreland

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