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Statistical Mechanics of Problems in Transcription Regulation

Citation

Morrison, Muir (2021) Statistical Mechanics of Problems in Transcription Regulation. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/d042-rp26. https://resolver.caltech.edu/CaltechTHESIS:06082021-005042886

Abstract

As the quantity of sequenced genome data continues to multiply, our understanding of the transcriptional regulation of genomes has lagged behind. This deficit impinges on research throughout biology, from fundamental questions of how evolution proceeds to eminently practical questions such as how antibiotic resistance arises.

In this thesis we present three threads that address the question of transcriptional regulation from distinct perspectives. The first thread focuses on the simplest nontrivial regulation motif common in bacteria. We analyze in turn a sampling of the myriad mathematical models previously proposed in the literature for this system. We attempt to shine light on the similarities and differences of the models’ predictions, clarify their microscopic interpretations, and offer guidance as to situations when one model or another should be preferred or even distinguishable.

The second thread considers a substantially more complicated genetic circuit, for which we build a minimal phenomenological model that retains intuitive microscopic meaning for all its parameters. The model neatly explains recent experimental observations of bistability in the circuit, and suggests natural generalizations to other metabolically important gene circuits with qualitatively similar architectures.

Motivation for the third thread comes from even more complicated transcriptional regulation problems with a multitude of regulatory proteins and binding sites, where even enumerating all possible DNA-protein complexes manually is a formidable challenge. Here we propose a method to tackle this complexity that uses ideas from quantum field theory to encode assembly rules for macromolecular complexes. By specifying a small set of rules, we avoid manual enumeration of the much larger set of complexes, allowing the formalism to automatically generate this set for us.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Transcription regulation ; Statistical mechanics
Degree Grantor:California Institute of Technology
Division:Physics, Mathematics and Astronomy
Major Option:Physics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Phillips, Robert B.
Thesis Committee:
  • Roukes, Michael Lee (chair)
  • Phillips, Robert B.
  • Thomson, Matthew
  • Van Valen, David A.
Defense Date:17 June 2020
Record Number:CaltechTHESIS:06082021-005042886
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:06082021-005042886
DOI:10.7907/d042-rp26
Related URLs:
URLURL TypeDescription
https://doi.org/10.1371/journal.pone.0226453DOIArticle adapted for Ch. 2
ORCID:
AuthorORCID
Morrison, Muir0000-0002-0768-7234
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:14260
Collection:CaltechTHESIS
Deposited By: Kathy Johnson
Deposited On:08 Jun 2021 21:54
Last Modified:17 Jun 2021 20:49

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