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Related Experiment Videos

Modelling biological complexity: a physical scientist's perspective.

Peter V Coveney1, Philip W Fowler

  • 1Centre for Computational Science, Department of Chemistry, University College London, Christopher Ingold Laboratories, 20 Gordon Street, London WC1H 0AJ, UK. p.v.coveney@ucl.ac.uk

Journal of the Royal Society, Interface
|July 20, 2006
PubMed
Summary
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Complexity and self-organization concepts enhance understanding of dynamical systems in systems biology. Multiscale modeling, particularly hybrid approaches, integrates molecular and cellular levels to address parameter uncertainties and explore biological phenomena across scales.

Area of Science:

  • * Physics and Biology
  • * Complexity and Self-Organization
  • * Systems Biology

Background:

  • * Dynamical systems are increasingly understood through complexity and self-organization.
  • * Traditional modeling of biological systems like heart dynamics and circadian rhythms faces challenges with unknown parameters.
  • * Differences in scientific philosophies between physical and biological sciences influence complexity studies.

Purpose of the Study:

  • * To explore how complexity and self-organization concepts can advance systems biology.
  • * To investigate multiscale modeling approaches for integrating biological scales.
  • * To address computational challenges in modeling complex biological systems.

Main Methods:

  • * Numerical solutions for nonlinear coupled differential equations.

Related Experiment Videos

  • * Hierarchical and hybrid multiscale modeling strategies.
  • * Development of a hybrid continuum-molecular model (HybridMD) with a molecular insertion algorithm.
  • * Utilization of computational grids for large-scale simulations.

    • Main Results:

    • * Hybrid multiscale modeling offers a systematic approach to coupling models across length and time scales.
    • * Multiscale models can link molecular-level behavior to cellular-level effects, aiding in parameter calculation.
    • * HybridMD facilitates the integration of molecular and coarse-grained representations.
    • * Computational grids show promise for enhancing the scale of complex systems research.

    Conclusions:

    • * Multiscale modeling is crucial for systems biology, enabling study across diverse scales.
    • * Hybrid approaches are essential for integrating different levels of biological organization.
    • * Advanced computational tools and grid technologies are vital for the future of complex systems research in biology.