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dc.contributor.authorWang, Pengyuanen
dc.date.accessioned2014-03-14T20:38:29Zen
dc.date.available2014-03-14T20:38:29Zen
dc.date.issued2008-05-07en
dc.identifier.otheretd-05242008-153640en
dc.identifier.urihttp://hdl.handle.net/10919/33199en
dc.description.abstractDuring the past decade, many successful deterministic models of macromolecular regulatory networks have been built. Deterministic simulations of these models can show only average dynamics of the systems. However, stochastic simulations of macromolecular regulatory models can account for behaviors that are introduced by the noisy nature of the systems but not revealed by deterministic simulations. Thus, converting an existing model of value from the most common deterministic formulation to one suitable for stochastic simulation enables further investigation of the regulatory network. Although many different stochastic models can be developed and evolved from deterministic models, a direct conversion is the first step in practice. This conversion process is tedious and error-prone, especially for complex models. Thus, we seek to automate as much of the conversion process as possible. However, deterministic models often omit key information necessary for a stochastic formulation. Specifically, values in the model have to be scaled before a complete conversion, and the scaling factors are typically not given in the deterministic model. Several functionalities helping model scaling and converting are introduced and implemented in the JigCell modeling environment. Our tool makes it easier for the modeler to include complete details as well as to convert the model. Stochastic simulations are known for being computationally intensive, and thus require high performance computing facilities to be practical. With parallel computation on Virginia Tech's System X supercomputer, we are able to obtain the first stochastic simulation results for realistic cell cycle models. Stochastic simulation results for several mutants, which are thought to be biologically significant, are presented. Successful deployment of the enhanced modeling environment demonstrates the power of our techniques.en
dc.publisherVirginia Techen
dc.relation.haspartthesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectbiomolecular regulatory networksen
dc.subjectpathway modelingen
dc.subjectstochastic simulationen
dc.titleBridging the Gap between Deterministic and Stochastic Modeling with Automatic Scaling and Conversionen
dc.typeThesisen
dc.contributor.departmentComputer Scienceen
dc.description.degreeMaster of Scienceen
thesis.degree.nameMaster of Scienceen
thesis.degree.levelmastersen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.disciplineComputer Scienceen
dc.contributor.committeechairCao, Yangen
dc.contributor.committeememberShaffer, Clifford A.en
dc.contributor.committeememberWatson, Layne T.en
dc.contributor.committeememberBaumann, William T.en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05242008-153640/en
dc.date.sdate2008-05-24en
dc.date.rdate2008-06-17en
dc.date.adate2008-06-17en


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