title: Asynchronous adaptive time step in quantitative cellular automata modeling.
abstract: BACKGROUND
  The behaviors of cells in metazoans are context dependent, thus large-scale multi-cellular modeling is often necessary, for which cellular automata are natural candidates. Two related issues are involved in cellular automata based multi-cellular modeling: how to introduce differential equation based quantitative computing to precisely describe cellular activity, and upon it, how to solve the heavy time consumption issue in simulation.
  
  
  RESULTS
  Based on a modified, language based cellular automata system we extended that allows ordinary differential equations in models, we introduce a method implementing asynchronous adaptive time step in simulation that can considerably improve efficiency yet without a significant sacrifice of accuracy. An average speedup rate of 4-5 is achieved in the given example.
  
  
  CONCLUSIONS
  Strategies for reducing time consumption in simulation are indispensable for large-scale, quantitative multi-cellular models, because even a small 100 x 100 x 100 tissue slab contains one million cells. Distributed and adaptive time step is a practical solution in cellular automata environment.
authors.affiliation: Bioinformatics Institute, National University of Singapore, 138671. zhuhao@bii.a-star.edu.sg
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
authors.forename: Peter Y H
authors.initials: PY
authors.lastname: Pang
authors.forename: Yan
authors.initials: Y
authors.lastname: Sun
authors.forename: Pawan
authors.initials: P
authors.lastname: Dhar
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation / Q000639:trends; D004594:Electrophysiology / Q000379:methods; D008954:Models, Biological; D009206:Myocardium / Q000166:cytology* / Q000378:metabolism*; D013995:Time