Computational 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.<p>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.
| Identifer | oai:union.ndltd.org:USASK/oai:usask.ca:etd-03112008-203621 |
| Date | 17 March 2008 |
| Creators | Koh, Chu Shin |
| Contributors | Wang, Edwin, Selvaraj, Gopalan, Kusalik, Anthony J. (Tony), Wu, Fang-Xiang |
| Publisher | University of Saskatchewan |
| Source Sets | University of Saskatchewan Library |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://library.usask.ca/theses/available/etd-03112008-203621/ |
| Rights | unrestricted, I 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 University of Saskatchewan 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. |
Page generated in 0.0023 seconds