Computational Models of Function and Evolution of cis-Regulatory Sequences

cis-regulatory modules;transcription factor binding sites;comparative genomics;probabilistic modeling;sequence evolution;thermodynamic modeling;segmentation

He, Xin

关键词: cis-regulatory modules;    transcription factor binding sites;    comparative genomics;    probabilistic modeling;    sequence evolution;    thermodynamic modeling;    segmentation;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/14601/1_He_Xin.pdf?sequence=3&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Gene expression is controlled by regulatory DNA sequences, often called cis-regulatory modules or CRMs in higher organisms. Even though complete genomes are available in many species, a catalog of CRMs is far from complete. Meanwhile, how basic building blocks of CRMs, called transcription factor binding sites (TFBSs), coordinate to drive gene expression is unclear. My thesis is focused on predicting the location of CRMs in genomes and understanding their function and evolution through computational methods. The first part of my thesis developed a comparative genomic method of CRM prediction. This method is based on a probabilistic model of CRM evolution, capturing the constraint as well as turnover of TFBSs during evolution. Through a statistical approach that marginalizes hidden variables, the method is able to deal with the uncertainty of sequence alignment and prediction of individual TFBSs, two primary technical hurdles of existing methods. In a related work, I collaborated with a graduate colleague to study the empirical evolutionary pattern of TFBSs, taking advantage of the recently available 12 Drosophila genomes. We found, among other things, that the evolution of binding sites is constrained by the affinities to their cognate TFs. The second part of my thesis developed predictive models of gene regulation based on physical principles. One such method is able to analyze large scale TF-DNA binding data to identify cooperative interactions of TFs, to explore the effects of sequence organization on the TF interactions and to study the conservation of TF-binding affinities of sequences. The model we developed for predicting expression patterns of CRMs advances existing workby incorporating a number of mechanistic aspects of transcriptional regulation, including cooperative binding of TFs, the synergism among multiple activators and the short-range repression, where repressors block the function of adjacent activator sites. This allows us to gain understandings of the regulatory process in Drosophila segmentation, for instance, both the cooperative interactions among activator molecules and their synergistic interaction with the transcriptional machinery are important in determining the expression patterns.