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dc.contributor.authorWang, Pengyuanen_US
dc.date.accessioned2014-03-14T20:38:29Z
dc.date.available2014-03-14T20:38:29Z
dc.date.issued2008-05-07en_US
dc.identifier.otheretd-05242008-153640en_US
dc.identifier.urihttp://hdl.handle.net/10919/33199
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.

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dc.publisherVirginia Techen_US
dc.relation.haspartthesis.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectbiomolecular regulatory networksen_US
dc.subjectpathway modelingen_US
dc.subjectstochastic simulationen_US
dc.titleBridging the Gap between Deterministic and Stochastic Modeling with Automatic Scaling and Conversionen_US
dc.typeThesisen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Scienceen_US
dc.contributor.committeechairCao, Yangen_US
dc.contributor.committeememberShaffer, Clifford A.en_US
dc.contributor.committeememberWatson, Layne T.en_US
dc.contributor.committeememberBaumann, William T.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-05242008-153640/en_US
dc.date.sdate2008-05-24en_US
dc.date.rdate2008-06-17
dc.date.adate2008-06-17en_US


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