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Stochastic Simulation of Multiscale Reaction-Diffusion Models via First Exit TimesPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2016 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Uppsala: Acta Universitatis Upsaliensis, 2016. , p. 53
##### Series

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1376
##### Keyword [en]

computational systems biology, diffusion, first exit times, unstructured meshes, reaction-diffusion master equation, macromolecular crowding, excluded volume effects, finite element method, backward analysis, stochastic simulation
##### National Category

Computational Mathematics
##### Research subject

Scientific Computing with specialization in Numerical Analysis
##### Identifiers

URN: urn:nbn:se:uu:diva-284085ISBN: 978-91-554-9582-4 (print)OAI: oai:DiVA.org:uu-284085DiVA, id: diva2:921108
##### Public defence

2016-06-10, ITC 2446, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
##### Opponent

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt432",{id:"formSmash:j_idt432",widgetVar:"widget_formSmash_j_idt432",multiple:true});
##### Supervisors

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#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt444",{id:"formSmash:j_idt444",widgetVar:"widget_formSmash_j_idt444",multiple:true});
##### Funder

Swedish Research Council, 621- 2001-3148NIH (National Institute of Health), 1R01EB014877-01
Available from: 2016-05-19 Created: 2016-04-14 Last updated: 2016-06-01Bibliographically approved
##### List of papers

Mathematical models are important tools in systems biology, since the regulatory networks in biological cells are too complicated to understand by biological experiments alone. Analytical solutions can be derived only for the simplest models and numerical simulations are necessary in most cases to evaluate the models and their properties and to compare them with measured data.

This thesis focuses on the mesoscopic simulation level, which captures both, space dependent behavior by diffusion and the inherent stochasticity of cellular systems. Space is partitioned into compartments by a mesh and the number of molecules of each species in each compartment gives the state of the system. We first examine how to compute the jump coefficients for a discrete stochastic jump process on unstructured meshes from a first exit time approach guaranteeing the correct speed of diffusion. Furthermore, we analyze different methods leading to non-negative coefficients by backward analysis and derive a new method, minimizing both the error in the diffusion coefficient and in the particle distribution.

The second part of this thesis investigates macromolecular crowding effects. A high percentage of the cytosol and membranes of cells are occupied by molecules. This impedes the diffusive motion and also affects the reaction rates. Most algorithms for cell simulations are either derived for a dilute medium or become computationally very expensive when applied to a crowded environment. Therefore, we develop a multiscale approach, which takes the microscopic positions of the molecules into account, while still allowing for efficient stochastic simulations on the mesoscopic level. Finally, we compare on- and off-lattice models on the microscopic level when applied to a crowded environment.

1. Stochastic diffusion processes on Cartesian meshes$(function(){PrimeFaces.cw("OverlayPanel","overlay866453",{id:"formSmash:j_idt480:0:j_idt484",widgetVar:"overlay866453",target:"formSmash:j_idt480:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Simulation of stochastic diffusion via first exit times$(function(){PrimeFaces.cw("OverlayPanel","overlay866290",{id:"formSmash:j_idt480:1:j_idt484",widgetVar:"overlay866290",target:"formSmash:j_idt480:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Analysis and design of jump coefficients in discrete stochastic diffusion models$(function(){PrimeFaces.cw("OverlayPanel","overlay893382",{id:"formSmash:j_idt480:2:j_idt484",widgetVar:"overlay893382",target:"formSmash:j_idt480:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Multiscale modeling of diffusion in a crowded environment$(function(){PrimeFaces.cw("OverlayPanel","overlay919446",{id:"formSmash:j_idt480:3:j_idt484",widgetVar:"overlay919446",target:"formSmash:j_idt480:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. Excluded volume effects in on- and off-lattice reaction–diffusion models$(function(){PrimeFaces.cw("OverlayPanel","overlay921107",{id:"formSmash:j_idt480:4:j_idt484",widgetVar:"overlay921107",target:"formSmash:j_idt480:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

isbn
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