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Hybrid Stochastic Simulation of Rule-Based Polymerization Models

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Hybrid Systems Biology (HSB 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9957))

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Abstract

Modeling and simulation of polymer formation is an important field of research not only in the material sciences but also in the life sciences due to the prominent role of processes such as actin filament formation and multivalent ligand-receptor interactions. While the advantages of a rule-based description of polymerizations has been successfully demonstrated, no efficient simulation of these mostly stiff processes is currently available, in particular for large system sizes.

We present a hybrid stochastic simulation approach, in which the average changes of highly abundant species due to fast reactions are deterministically simulated while for the remaining species with small counts a rule-based simulation is performed. We propose a nesting of rejection steps to arrive at an approach that is efficient and accurate. We test our method on two case studies of polymerization.

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Notes

  1. 1.

    Here, we consider only a single product pattern for rules, since reactions with two products can still be described by a single product pattern. In such a case, the pattern does not represent one connected chemical structure, but the structure misses a bond and can therefore be divided into two substructures.

  2. 2.

    Concentrations can be converted to counts and vice versa as described in [28], just by using the factor \(V\cdot N_A\), where V is the volume of the system and \(N_A\) is Avogadro constant.

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Acknowledgments

This work was funded by the Cluster of Excellence on Multimodal Computing and Interaction (MMCI) at Saarland University, Germany.

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Authors and Affiliations

  1. Modeling and Simulation Group, Saarland University, Saarbrücken, Germany

    Thilo Krüger & Verena Wolf

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  1. Thilo Krüger
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  2. Verena Wolf
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Correspondence to Thilo Krüger .

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Editors and Affiliations

  1. INRIA Grenoble - Rhone-Alpes , Saint-Ismier, France

    Eugenio Cinquemani

  2. Electrical Engineering/Computer Science, University of California at Berkeley , Berkeley, California, USA

    Alexandre Donzé

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Krüger, T., Wolf, V. (2016). Hybrid Stochastic Simulation of Rule-Based Polymerization Models. In: Cinquemani, E., Donzé, A. (eds) Hybrid Systems Biology. HSB 2016. Lecture Notes in Computer Science(), vol 9957. Springer, Cham. https://doi.org/10.1007/978-3-319-47151-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-47151-8_3

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