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dc.contributor.authorLeaman, Eric Joshuaen_US
dc.date.accessioned2017-06-13T19:43:44Z
dc.date.available2017-06-13T19:43:44Z
dc.date.issued2016-12-09en_US
dc.identifier.otheretd-12202016-095118en_US
dc.identifier.urihttp://hdl.handle.net/10919/78072
dc.description.abstractSwimming bacteria are ubiquitous in aqueous environments ranging from oceans to fluidic environments within a living host. Furthermore, engineered bacteria are being increasingly utilized for a host of applications including environmental bioremediation, biosensing, and for the treatment of diseases. Often driven by chemotaxis (i.e. biased migration in response to gradients of chemical effectors) and quorum sensing (i.e. number density dependent regulation of gene expression), bacterial population dynamics and emergent behavior play a key role in regulating their own life and their impact on their immediate environment. Computational models that accurately and robustly describe bacterial population behavior and response to environmental stimuli are crucial to both understanding the dynamics of microbial communities and efficiently utilizing engineered microbes in practice. Many existing computational frameworks are finely-detailed at the cellular level, leading to extended computational time requirements, or are strictly population scale models, which do not permit population heterogeneities or spatiotemporal variability in the environment. To bridge this gap, we have created and experimentally validated a scalable, computationally-efficient, agent-based model of bacterial chemotaxis and quorum sensing (QS) which robustly simulates the stochastic behavior of each cell across a wide range of bacterial populations, ranging from a few to several hundred cells. We quantitatively and accurately capture emergent behavior in both isogenic QS populations and the altered QS response in a mixed QS and quorum quenching (QQ) microbial community. Finally, we show that the model can be used to predictively design synthetic genetic components towards programmed microbial behavior.
dc.language.isoen_USen_US
dc.publisherVirginia Techen_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.subjectquorum sensingen_US
dc.subjectcomputational biologyen_US
dc.subjectflagellated bacteriaen_US
dc.subjectquorum quenchingen_US
dc.subjectchemotaxisen_US
dc.titleAn Experimentally-validated Agent-based Model to Study the Emergent Behavior of Bacterial Communitiesen_US
dc.typeThesisen_US
dc.contributor.departmentMechanical Engineeringen_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.disciplineMechanical Engineeringen_US
dc.contributor.committeechairBehkam, Baharehen_US
dc.contributor.committeememberPaul, Marken_US
dc.contributor.committeememberSenger, Ryanen_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-12202016-095118/en_US
dc.date.sdate2016-12-20en_US
dc.date.rdate2017-02-03
dc.date.adate2017-02-03en_US


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