Shaping the bacterial transcriptome: Perspectives from kinetic modeling

Matter is governed by the physical and chemical laws of our universe – living matter is no exception. Yet, living matter appears to exploit these laws while its non-living counterpart is bound by them. Stochastic interactions orchestrated at various sub-cellular levels (genome, transcriptome, proteome, and metabolome) enable the cell to perform life’s fundamental processes such as growth, metabolism, proliferation, and stress-response. Of specific interest is the transcriptome, the "middle child" of the central dogma, defined as all forms of RNA (rRNA, tRNA, mRNA, sRNA, and ncRNA) making up the cell. The transcriptome is unique in that, like the genome, it encodes genetic information, but also similar to the proteome, is capable of executing biological tasks such as regulation. I will employ concepts of stochastic and deterministic kinetic modeling, systems biology, and bio-informatics, validated using mathematical principles to characterize the bacteria transcriptome in different bacterial systems focusing on the factors which shape the transcriptome. The goal of this document is to provide perspective on the bacterial world, specifically at the transcriptome level. I have divided my thesis into six chapters seeking to achieve this aim. I will begin by attempting to contextualize bacteria and our current physical and chemical understanding of them, and outline techniques used to computationally model the bacteria from a single reaction to an entire cell. I will additionally touch on some experimental characterization approaches that supply the aforementioned computational models with constraints to parameterize against. In the second chapter, I will describe a case study on the sugar stress response of the model bacterial organism, Escherichia coli, elucidating how an interaction at the transcriptome level is used to modulate the state of the cell. The third chapter will focus on how the simplest living bacterium supplies the individual components necessary to build the transcriptome probed via a kinetic description of an entire cell that couples the metabolism with the genetic information processing. Next, I will re-examine the whole-cell model of the third chapter and present a case for a modified genetic information processing model motivated by structure and sequence interactions related to the evolution and maintenance of the transcriptome. The fifth chapter, the final of the main chapters, will investigate similar but unique experimental techniques to characterize the transcriptome describing the transcriptional landscape within a minimized bacteria. Aided by the new insight, I will provide a suggested framework to improve the genetic information processing model of the fourth chapter by updating it with the experimental findings as frame of reference. Finally, I will conclude with a summary of the topics covered in the thesis and outline from my perspective what further explorations could be pursued in the immediate and distant future.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Chemistry

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2024

Author dc:creator

• Brier, Troy Anthony

Contributors dc:contributor

• Luthey-Schulten, Zaida
• Vanderpool, Carin K
• Chemla, Yann R
• Mehta, Angad P

• Transcriptomics
• Stochastic Gene Expression
• Kinetic Modeling
• Computational Biology
• Whole-cell Modeling
• Metabolism

Statement dc:rights

• Copyright 2024 Troy Anthony Brier

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/127480

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F127480