Stochastic Simulation Methods for Solving Systems with Multi-State Species

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dc.contributor.authorLiu, Zhenen
dc.contributor.committeechairCao, Yangen
dc.contributor.committeememberMurali, T. M.en
dc.contributor.committeememberSandu, Adrianen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2014-03-14T20:35:42Zen
dc.date.adate2009-05-29en
dc.date.available2014-03-14T20:35:42Zen
dc.date.issued2009-05-06en
dc.date.rdate2009-05-29en
dc.date.sdate2009-05-08en
dc.description.abstractGillespie's stochastic simulation algorithm (SSA) has been a conventional method for stochastic modeling and simulation of biochemical systems. However, its population-based scheme faces the challenge from multi-state situations in many biochemical models. To tackle this problem, Morton-Firth and Bray's stochastic simulator (StochSim) was proposed with a particle-based scheme. The thesis first provides a detailed comparison between these two methods, and then proposes improvements on StochSim and a hybrid method to combine the advantages of the two methods. Analysis and numerical experiment results demonstrate that the hybrid method exhibits extraordinary performance for systems with both the multi-state feature and a high total population. In order to deal with the combinatorial complexity caused by the multi-state situation, the rules-based modeling was proposed by Hlavacek's group and the particle-based Network-Free Algorithm (NFA) has been used for its simulation. In this thesis, we improve the NFA so that it has both the population-based and particle-based features. We also propose a population-based method for simulation of the rule-based models. The bacterial chemotaxis model has served as a good biological example involving multi-state species. We implemented different simulation methods on this model. Then we constructed a graphical interface and compared the behaviors of the bacterium under different mechanisms, including simplified mathematical models and chemically reacting networks which are simulated stochastically.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-05082009-031719en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05082009-031719/en
dc.identifier.urihttp://hdl.handle.net/10919/32392en
dc.publisherVirginia Techen
dc.relation.haspartetd.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectStochSimen
dc.subjectrule-based modelingen
dc.subjectmulti-stateen
dc.subjectSSAen
dc.subjecthybrid methoden
dc.titleStochastic Simulation Methods for Solving Systems with Multi-State Speciesen
dc.typeThesisen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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