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    BMC Bioinformatics. 2004 Jun 29;5:85.

    Asynchronous adaptive time step in quantitative cellular automata modeling.

    Zhu H, Pang PY, Sun Y, Dhar P.

    Bioinformatics Institute, National University of Singapore, 138671. zhuhao@bii.a-star.edu.sg

    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.

    PMID: 15222901 [PubMed - indexed for MEDLINE]

    PMCID: PMC459211

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