Genomic methods for studying the post-translational regulation of transcription factors / Logan J. Everett.

Format:

Book

Manuscript

Thesis/Dissertation

Author/Creator:

Everett, Logan J.

Contributor:

Hannenhalli, Sridhar, advisor.

Master, Stephen R., advisor.

University of Pennsylvania.

Language:

English

Subjects (All):

Penn dissertations--Genomics and computational biology.

Genomics and computational biology--Penn dissertations.

Academic Dissertations as Topic.

Medical Subjects:

Academic Dissertations as Topic.

Local Subjects:

Penn dissertations--Genomics and computational biology.

Genomics and computational biology--Penn dissertations.

Physical Description:

xii, 196 pages : illustrations (some color) ; 29 cm

Production:

2010.

Summary:

The spatiotemporal coordination of gene expression is a fundamental process in cellular biology. Gene expression is regulated, in large part, by sequence-specific transcription factors that bind to DNA regions in the proximity of each target gene. Transcription factor activity and specificity are, in turn, regulated posttranslationally by protein-modifying enzymes. High-throughput methods exist to probe specific steps of this process, such as protein-protein and protein-DNA interactions, but few computational tools exist to integrate this information in a principled, model-oriented manner. In this work, I develop several computational tools for studying the functional implications of transcription factor modification. I establish the first publicly accessible database for known and predicted regulatory circuits that encompass modifying enzymes, transcription factors, and transcriptional targets. I also develop a model-based method for integrating heterogeneous genomic and proteomic data for the inference of modification-dependent transcriptional regulatory networks. The model-based method is thoroughly validated as a reliable and accurate computational genomic tool. Additionally, I propose and demonstrate fundamental improvements to computational proteomic methods for identifying modified protein forms. In summary, this work contributes critical methodological advances to the field of regulatory network inference.

Notes:

Advisers: Sridhar Hannenhalli; Stephen R. Master.

Thesis (Ph.D. in Genomics and Computational Biology) -- University of Pennsylvania, 2010.

Includes bibliographical references.