Coarse-Grained Brownian Dynamics Simulation of Rule-Based Models

  • Michael Klann
  • Loïc Paulevé
  • Tatjana Petrov
  • Heinz Koeppl
Conference paper

DOI: 10.1007/978-3-642-40708-6_6

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8130)
Cite this paper as:
Klann M., Paulevé L., Petrov T., Koeppl H. (2013) Coarse-Grained Brownian Dynamics Simulation of Rule-Based Models. In: Gupta A., Henzinger T.A. (eds) Computational Methods in Systems Biology. CMSB 2013. Lecture Notes in Computer Science, vol 8130. Springer, Berlin, Heidelberg

Abstract

Studying spatial effects in signal transduction, such as co-localization along scaffold molecules, comes at a cost of complexity. In this paper, we propose a coarse-grained, particle-based spatial simulator, suited for large signal transduction models. Our approach is to combine the particle-based reaction and diffusion method, and (non-spatial) rule-based modeling: the location of each molecular complex is abstracted by a spheric particle, while its internal structure in terms of a site-graph is maintained explicit. The particles diffuse inside the cellular compartment and the colliding complexes stochastically interact according to a rule-based scheme. Since rules operate over molecular motifs (instead of full complexes), the rule set compactly describes a combinatorial or even infinite number of reactions. The method is tested on a model of Mitogen Activated Protein Kinase (MAPK) cascade of yeast pheromone response signaling. Results demonstrate that the molecules of the MAPK cascade co-localize along scaffold molecules, while the scaffold binds to a plasma membrane bound upstream component, localizing the whole signaling complex to the plasma membrane. Especially we show, how rings stabilize the resulting molecular complexes and derive the effective dissociation rate constant for it.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael Klann
    • 1
  • Loïc Paulevé
    • 1
  • Tatjana Petrov
    • 1
  • Heinz Koeppl
    • 1
    • 2
  1. 1.BISON Group, Automatic Control LaboratoryETH ZurichZurichSwitzerland
  2. 2.IBM Research - ZurichRueschlikonSwitzerland

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