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dc.contributor.advisorSelvaraj, Gopalanen_US
dc.contributor.advisorKusalik, Anthony J. (Tony)en_US
dc.creatorKoh, Chu Shinen_US
dc.date.accessioned2008-03-11T20:36:21Zen_US
dc.date.accessioned2013-01-04T04:26:31Z
dc.date.available2008-03-17T08:00:00Zen_US
dc.date.available2013-01-04T04:26:31Z
dc.date.created2008-03en_US
dc.date.issued2008-03-17en_US
dc.date.submittedMarch 2008en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-03112008-203621en_US
dc.description.abstractComputational gene regulation models provide a means for scientists to draw biological inferences from large-scale gene expression data. The expression data used in the models usually are obtained in a time series in response to an initial perturbation. The common objective is to reverse engineer the internal structure and function of the genetic network from observing and analyzing its output in a time-based fashion. In many studies (Wang [39], Resendis-Antonio [31]), each gene is considered to have a regulatory effect on another gene. A network association is created based on the correlation of expression data. Highly correlated genes are thought to be co-regulated by similar (if not the same) mechanism. Gene co-regulation network models disregard the cascading effects of regulatory genes such as transcription factors, which could be missing in the expression data or are expressed at very low concentrations and thus undetectable by the instrument. As an alternative to the former methods, some authors (Wu et al. [40], Rangel et al. [28], Li et al. [20]) have proposed treating expression data solely as observation values of a state-space system and derive conceptual internal regulatory elements, i.e. the state-variables, from these measurements. This approach allows one to model unknown biological factors as hidden variables and therefore can potentially reveal more complex regulatory relations.In a preliminary portion of this work, two state-space models developed by Rangel et al. and Wu et al. respectively were compared. The Rangel model provides a means for constructing a statistically reliable regulatory network. The model is demonstrated on highly replicated Tcell activation data [28]. On the other hand, Wu et al. develop a time-delay module that takes transcriptional delay dynamics into consideration. The model is demonstrated on non-replicated yeast cell-cycle data [40]. Both models presume time-invariant expression data. Our attempt to use the Wu model to infer small gene regulatory network in yeast was not successful. Thus we develop a new modeling tool incorporating a time-lag module and a novel method for constructing regulatory networks from non-replicated data. The latter involves an alternative scheme for determining network connectivity. Finally, we evaluate the networks generated from the original and extended models based on a priori biological knowledge.en_US
dc.language.isoen_USen_US
dc.subjectdelaysen_US
dc.subjectgene regulatory networksen_US
dc.subjectnetworksen_US
dc.subjectstate-spaceen_US
dc.titleModeling gene regulatory networks using a state-space model with time delaysen_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US
dc.type.materialtexten_US
dc.type.genreThesisen_US
dc.contributor.committeeMemberWang, Edwinen_US
dc.contributor.committeeMemberWu, Fang-Xiangen_US


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