Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2105-5-85
Title: Asynchronous adaptive time step in quantitative cellular automata modeling
Authors: Zhu, H.
Pang, P.Y.H. 
Sun, Y.
Dhar, P.
Issue Date: 29-Jun-2004
Citation: Zhu, H., Pang, P.Y.H., Sun, Y., Dhar, P. (2004-06-29). Asynchronous adaptive time step in quantitative cellular automata modeling. BMC Bioinformatics 5 : -. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2105-5-85
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 × 100 × 100 tissue slab contains one million cells. Distributed and adaptive time step is a practical solution in cellular automata environment. © 2004 Zhu et al; licensee BioMed Central Ltd.
Source Title: BMC Bioinformatics
URI: http://scholarbank.nus.edu.sg/handle/10635/102903
ISSN: 14712105
DOI: 10.1186/1471-2105-5-85
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