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PRIORITIZING PHENOTYPE-ASSOCIATED FUNCTIONAL MODULES AND
SUB-NETWORKS FROM HIGH THROUGHOUT SCREENING RESULTS
by
Li Wang
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTATIONAL BIOLOGY)
August 2009
Copyright 2009 Li Wang
Object Description
| Title | Prioritizing phenotype-associated functional modules and sub-networks from high throughout screening results |
| Author | Wang, Li |
| Author email | wang7@usc.edu; tujiaojiao1981@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Molecular & Computational Biology |
| School | College of Letters, Arts and Sciences |
| Date defended/completed | 2009-03-13 |
| Date submitted | 2009 |
| Restricted until | Unrestricted |
| Date published | 2009-08-03 |
| Advisor (committee chair) | Sun, Fengzhu |
| Advisor (committee member) |
Chen, Liang Arbeitman, Michelle Kuo, C.C. Jay |
| Abstract | How to interpret the nature of biological processes, which when perturbed cause certain phenotype changes, such as human disease, is a major challenge. The completion of sequencing the genomics of many model organisms has made “reverse genetic approaches” efficient and comprehensive ways to identify the causal genes for a given phenotype under investigation. For instance, genome-wide knockout strains are now available for S. cerevisiae, and diverse high throughput RNAi knockdown experiments have been performed for multiple higher organisms. Although very useful, these high throughput screening approaches are associated with two main problems: 1) the underlying biology, i.e., how genetic perturbation leads to the change of phenotypes in the complex of biological systems is unclear; 2) the screening results could be very noisy with high false positive and false negative rates.; As genomic data from different sources accumulates, integrating screening results with other genomic information, particularly in forms of functional modules and networks, might help solve the above problems. Motivated by this, we developed two novel integrative computational approaches to address problems with high throughput screening results. In our first study, we represented the biological system as a set of predefined functional modules, and developed a Bayesian Network strategy to identify functional modules that are causally implicated in the phenotype under investigation. In the second study, we represented the biological system as a network of functional or physically associated proteins. We developed a network-based scoring system to filter out noise in RNAi screens.; Our studies demonstrate that the modular and network information can be effectively utilized to address potential problems with high throughput screening results and to achieve what would otherwise be impossible from the screening results alone. |
| Keyword | network; modules; phenotype; high throughput screenings |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m2450 |
| Rights | Wang, Li |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Wang-2952 |
| Archival file | uscthesesreloadpub_Volume14/etd-Wang-2952.pdf |
Description
| Title | Page 1 |
| Full text | PRIORITIZING PHENOTYPE-ASSOCIATED FUNCTIONAL MODULES AND SUB-NETWORKS FROM HIGH THROUGHOUT SCREENING RESULTS by Li Wang A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTATIONAL BIOLOGY) August 2009 Copyright 2009 Li Wang |
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