Evolution of Transcriptional Regulatory Sequences Dissertation Thesis


Author(s): Tuğrul, Murat
Advisor(s): Barton, Nicholas H; Tkačik, Gašper
Committee Chair(s): Tkačik, Gašper
Committee Member(s): Jaeger, Johannes; Guet, Calin
Title: Evolution of Transcriptional Regulatory Sequences
Affiliation IST Austria
Abstract: 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.
Publication Title: IST Dissertation
Degree Granting Institution: IST Austria  
Degree: PhD
Degree Date: 2016-07-01
Notes: This PhD thesis may not have been completed without the help and care I received from some peo- ple during my PhD life. I am especially grateful to Tiago Paixao, Gasper Tkacik, Nick Barton, not only for their scientific advices but also for their patience and support. I thank Calin Guet and Jonathan Bollback for allowing me to “play around” in their labs and get some experience on experimental evolution. I thank Magdalena Steinrueck and Fabienne Jesse for collaborating and sharing their experimental data with me. I thank Johannes Jaeger for reviewing my thesis. I thank all members of Barton group (aka bartonians) for their feedback, and all workers of IST Austria for making the best working conditions. Lastly, I thank two special women, Nejla Sag ̆lam and Setenay Dog ̆an, for their continuous support and encouragement. I truly had a great chance of having right people around me.
Open access: no