The challenges of interfacing between biological knowledge and computational models

27 October 2006

Martin Meier-Schellersheim
Laboratory of Immunology


The deep scepticism that most experimental biologists have towards computational modeling in biology is rooted in the fact that computational models are frequently overly simplified caricatures of the biological processes they are supposed to represent. Two main factors account for those simplifications:

  1. limitations in computing power and simulation algorithms and
  2. the difficulty of creating formalized representations of the components and interactions in biological systems.
I would like to focus on the second aspect and discuss the question of how we can provide ways for (experimental) biologists to turn their knowledge and hypotheses about how cells behave into formal descriptions that can be investigated through quantitative simulations.


M. Meier-Schellersheim, X. Xu, B. Angermann, E.J. Kunkel, T. Jin & R.N. Germain, "Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method", PLOS Comp Biol, 2:710-24 2006. PubMed

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