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dc.contributor.authorHofmann, Ariane Leonien_US
dc.date.accessioned2017-04-04T19:49:39Z
dc.date.available2017-04-04T19:49:39Z
dc.date.issued2012-07-31en_US
dc.identifier.otheretd-08132012-160727en_US
dc.identifier.urihttp://hdl.handle.net/10919/76841
dc.description.abstractStochastic modeling to represent intrinsic and extrinsic noise is an important challenge in molecular systems biology. There are numerous ways to model intrinsic noise. One framework for intrinsic noise in gene regulatory networks was recently proposed within the discrete setting. In contrast, extrinsic perturbations were rarely modeled due to the complex mechanisms that contribute to its emergence. Here a discrete framework to model extrinsic noise is proposed. The interacting species of the model are represented by discrete variables and are perturbed to represent extrinsic noise. In particular, they are subject to a discretized lognormal distribution. Additionally, a delay is imposed on the update with a certain probability. These two perturbations represent global extrinsic noise and pathway-specic extrinsic noise. It leads to large variations in the concentration of proteins, which is consistent with an existing continuous way of modeling extrinsic fluctuations. The framework is applied to three different published discrete models: the cell fate of lambda phage infection of bacteria, the lactose utilization system in E. coli, and a signaling network in melanoma cells. The framework captures factors that signicantly contribute to the random decision between lysis and lysogeny as well as explains the bistable switch in the model of the lac operon. Finally, a feed-forward loop analysis is conducted by measuring and comparing the noise level in the target protein of feed-forward loops. This analysis reveals the ability of certain feed-forward loops to attenuate or amplify fluctuations, dependent upon various levels of noise. In conclusion, this thesis aims to resolve the question of how the extrinsic noise can be modeled and how biological systems are able to maintain functionality in the wake of such large variations.
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.subjectmathematical modelingen_US
dc.subjectextrinsic noiseen_US
dc.subjectgene regulatory networksen_US
dc.titleA Stochastic Framework to Model Extrinsic Noise in Gene Regulatory Networksen_US
dc.typeThesisen_US
dc.contributor.departmentMathematicsen_US
dc.description.degreeM.S.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
dc.contributor.committeechairLaubenbacher, Reinhard C.en_US
dc.contributor.committeememberBurns, John A.en_US
dc.contributor.committeememberCiupe, Mihaela Stacaen_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://theses.lib.vt.edu/theses/available/etd-08132012-160727/en_US
dc.date.sdate2012-08-13en_US
dc.date.rdate2016-09-30
dc.date.adate2012-09-05en_US


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