Pusat Studi Cellular Automata

Author:

• G. Bard Ermentrout, Department of Mathematics and statistics, University of Pittsburgh, USA
• Leah Edelstein-Keshet, Department of Mathematics, University of British Columbia, Vancouver, Canada
  

Content:

We review a number of biologically motivated cellular automata (CA) that arise in models of excitable and oscillatory media, in developmental biological, in neurobiology, and in population biology. We suggest technical and theoritical arguments that permit greater speed and enhanced realism, and apply these to several classical examples of pattern formation. We also describe CA that arise in models for fibroblast aggregation, branching networks, trail following, and neuronal maps.

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• biological-model-ca

Author:

1. B. Ribba, Institute for Medical BioMathematics,and Clinical Pharmacology Unit, Faculty of Medicine
2. T. Alarc´on, Centre for Mathematical Biology, Mathematical Institute, University of Oxford, United Kingdom, and Bioinformatics Unit, Department of Computer Science, University College London, Gower Street, London
3. K. Marron, Institute for Medical BioMathematics,
4. P.K. Maini, Centre for Mathematical Biology, Mathematical Institute, University of Oxford, United Kingdom
5. Z. Agur, Institute for Medical BioMathematics.

Content:

The Hybrid Cellular Automata (HCA) modelling framework can be an efficient approach to a number of biological problems, particularly those which involve the integration of multiple spatial and temporal scales. As such, HCA may become a key modelling tool in the development of the so-called integrative biology. In this paper, we first discuss HCA on a general level and then present results obtained when this approach was implemented in cancer research.

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• cancer-therapy-ca

Author

• Huma Lodhi, Department of Computing, Imperial College London, UK, 2009

Content

Computational Systems Biology, a rapidly evolving field, is at the interface of computer science, mathematics, physics, and biology. It endeavours to study,analyze and understand complex biological systems by taking a coordinated integrated systems view using computational methodologies.

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• advances-biology-ca