Degree Type

Dissertation

Date of Award

2013

Degree Name

Doctor of Philosophy

Department

Electrical and Computer Engineering

Major

Bioinformatics and Computational Biology

First Advisor

Julie A. Dickerson

Second Advisor

Laura Jarboe

Abstract

Biological regulatory system is complex and involves many types of interactions, including transcriptional regulations, protein interactions, metabolic reactions and etc., to ensure the regulations of biological organisms. These regulations forms complex networks and play important roles in living organisms to adapt to the environment, control the rate of growth, and develop different phenotypes accordingly to its life cycle and the surrounding environment. Many of mechanisms and interactions of these networks are still not clear. Although better understanding of the regulatory systems is very important for biological research and engineering, to systematically reconstruct, analyze and integrate the complex regulatory systems is always challenging.

At first, a novel method to reconstruct gene regulatory networks (GRNs) was developed, implemented, tested, and applied to experimental data. This method introduced a hidden transcription factor activity (TFA) layer to the conventional GRN reconstruction methods. The testing results showed significantly improved network reconstruction precision and recall comparing to conventional methods. The Application to E. coli transcriptome experimental data demonstrated the potential biological significance of the reconstructed network.

A three level analysis framework to analyze TFAs and GRNs under different experimental conditions was followed up. The first level analyzes TFA patterns of individual transcription factors. The second level uses enrichment test and summarizes TFA behaviors by groups and their properties. The third level identifies key TFs of each experimental condition using network based analysis approach on effective regulatory network (ERN), a newly proposed differencial regulatory network model between experimental conditions. This analysis framework expands the traditional transcriptome data analysis to TFA and GRN level. The application to E. coli data showed the biological meaningfulness and helpfulness of analyzing transcriptome data on TFA and GRN level.

At last, a comprehensive regulatory focused regulatory system model for E. coli had been constructed by integrating transcriptional regulatory networks, protein interaction networks, metabolic reaction networks, and all other related regulations. Statistical tests and network property analysis of this constructed network revealed the connection between biological functions and the special network properties of the constructed network. And simulations of the regulatory signal response of this constructed network verified the biological meaningfulness of this network.

DOI

https://doi.org/10.31274/etd-180810-3120

Copyright Owner

Yao Fu

Language

en

File Format

application/pdf

File Size

191 pages

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