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Evolution of transcriptional regulatory sequences

By: Tugrul, Murat.
Material type: materialTypeLabelBookPublisher: IST Austria 2016
Contents:
Biographical Sketch
List of Publications
Acknowledgments
Abstract
List of Figures
1 General Introduction
2 Dynamics of transcriptional factor binding site evolution
3 Binding site evolution of bacterial RNA polymerase
4 Conclusions
Bibliography
Summary: Evolution of gene regulation is important for phenotypic evolution and diversity. Sequence-specific binding of regulatory proteins is one of the key regulatory mechanisms determining gene expression. Although there has been intense interest in evolution of regulatory binding sites in the last decades, a theoretical understanding is far from being complete. In this thesis, I aim at a better understanding of the evolution of transcriptional regulatory binding sequences by using biophysical and population genetic models. In the first part of the thesis, I discuss how to formulate the evolutionary dynamics of binding se- quences in a single isolated binding site and in promoter/enhancer regions. I develop a theoretical framework bridging between a thermodynamical model for transcription and a mutation-selection-drift model for monomorphic populations. I mainly address the typical evolutionary rates, and how they de- pend on biophysical parameters (e.g. binding length and specificity) and population genetic parameters (e.g. population size and selection strength). In the second part of the thesis, I analyse empirical data for a better evolutionary and biophysical understanding of sequence-specific binding of bacterial RNA polymerase. First, I infer selection on regulatory and non-regulatory binding sites of RNA polymerase in the E. coli K12 genome. Second, I infer the chemical potential of RNA polymerase, an important but unknown physical parameter defining the threshold energy for strong binding. Furthermore, I try to understand the relation between the lac promoter sequence diversity and the LacZ activity variation among 20 bacterial isolates by constructing a simple but biophysically motivated gene expression model. Lastly, I lay out a statistical framework to predict adaptive point mutations in de novo promoter evolution in a selection experiment.
List(s) this item appears in: IST Austria Thesis
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Thesis

Biographical Sketch

List of Publications

Acknowledgments

Abstract

List of Figures

1 General Introduction

2 Dynamics of transcriptional factor binding site evolution

3 Binding site evolution of bacterial RNA polymerase

4 Conclusions

Bibliography

Evolution of gene regulation is important for phenotypic evolution and diversity. Sequence-specific binding of regulatory proteins is one of the key regulatory mechanisms determining gene expression. Although there has been intense interest in evolution of regulatory binding sites in the last decades, a theoretical understanding is far from being complete. In this thesis, I aim at a better understanding of the evolution of transcriptional regulatory binding sequences by using biophysical and population genetic models. In the first part of the thesis, I discuss how to formulate the evolutionary dynamics of binding se- quences in a single isolated binding site and in promoter/enhancer regions. I develop a theoretical framework bridging between a thermodynamical model for transcription and a mutation-selection-drift model for monomorphic populations. I mainly address the typical evolutionary rates, and how they de- pend on biophysical parameters (e.g. binding length and specificity) and population genetic parameters (e.g. population size and selection strength). In the second part of the thesis, I analyse empirical data for a better evolutionary and biophysical understanding of sequence-specific binding of bacterial RNA polymerase. First, I infer selection on regulatory and non-regulatory binding sites of RNA polymerase in the E. coli K12 genome. Second, I infer the chemical potential of RNA polymerase, an important but unknown physical parameter defining the threshold energy for strong binding. Furthermore, I try to understand the relation between the lac promoter sequence diversity and the LacZ activity variation among 20 bacterial isolates by constructing a simple but biophysically motivated gene expression model. Lastly, I lay out a statistical framework to predict adaptive point mutations in de novo promoter evolution in a selection experiment.

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