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CellMC: a multiplatform model compiler for the Cell Broadband Engine and x86
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
2010 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 26, 426-428 p.Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2010. Vol. 26, 426-428 p.
National Category
Software Engineering Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-111276DOI: 10.1093/bioinformatics/btp662ISI: 000274342800026OAI: oai:DiVA.org:uu-111276DiVA: diva2:280200
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eSSENCE
Available from: 2009-12-08 Created: 2009-12-08 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Multiscale Stochastic Simulation of Reaction-Transport Processes: Applications in Molecular Systems Biology
Open this publication in new window or tab >>Multiscale Stochastic Simulation of Reaction-Transport Processes: Applications in Molecular Systems Biology
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Quantitative descriptions of reaction kinetics formulated at the stochastic mesoscopic level are frequently used to study various aspects of regulation and control in models of cellular control systems. For this type of systems, numerical simulation offers a variety of challenges caused by the high dimensionality of the problem and the multiscale properties often displayed by the biochemical model.

In this thesis I have studied several aspects of stochastic simulation of both well-stirred and spatially heterogenous systems. In the well-stirred case, a hybrid method is proposed that reduces the dimension and stiffness of a model. We also demonstrate how both a high performance implementation and a variance reduction technique based on quasi-Monte Carlo can reduce the computational cost to estimate the probability density of the system.

In the spatially dependent case, the use of unstructured, tetrahedral meshes to sample realizations of the stochastic process is proposed. Using such meshes, we then extend the reaction-diffusion framework to incorporate active transport of cellular cargo in a seamless manner. Finally, two multilevel methods for spatial stochastic simulation are considered. One of them is a space-time adaptive method combining exact stochastic, approximate stochastic and macroscopic modeling levels to reduce the simualation cost. The other method blends together mesoscale and microscale simulation methods to locally increase modeling resolution.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2011. 63 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 832
Keyword
stochastic simulation, chemical master equation, reaction-diffusion master equation, unstructured mesh, active transport, hybrid methods, URDME
National Category
Computational Mathematics
Research subject
Scientific Computing
Identifiers
urn:nbn:se:uu:diva-152098 (URN)978-91-554-8089-9 (ISBN)
Public defence
2011-06-10, Room 2446, Polacksbacken, Lägerhyddsvägen 2D, Uppsala, 10:15 (English)
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eSSENCE
Available from: 2011-05-19 Created: 2011-04-23 Last updated: 2012-01-26Bibliographically approved

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Hellander, Andreas

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