A fully interpretable approach to sequence-to-expression modeling
Bhogale, Shounak Girish
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https://hdl.handle.net/2142/120529
Description
Title
A fully interpretable approach to sequence-to-expression modeling
Author(s)
Bhogale, Shounak Girish
Issue Date
2023-04-26
Director of Research (if dissertation) or Advisor (if thesis)
Sinha, Saurabh
Doctoral Committee Chair(s)
Sinha, Saurabh
Committee Member(s)
Zhao, Dave
Shukla, Diwakar
Maslov, Sergei
Department of Study
School of Molecular & Cell Bio
Discipline
Biophysics & Quant Biology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Grn
Sequence-to-expression Modeling
Language
eng
Abstract
Gene expression regulation is a complex process where many regulatory elements such as transcription factors, enhancers, promoters, histone modifications work in tandem. In this thesis we focus our attention primarily on the interplay between two of the most important elements – transcription factors and enhancers. The binding of TFs to enhancers plays crucial role in modulating the gene expression. First, we present a thermodynamics-based sequence-to- expression model to study Drosophila mesodermal differentiation program. This model suggests indirect interaction between a transcription factor and DNA as a plausible mechanism for a transcription factor having dual regulatory roles (as an activator and a repressor) based on its environment. In the second part we discuss a neural network-based sequence-to-expression model to study neuronal stem cell differentiation program. The model learns the effect of transcription factors on gene expression across multiple cell types. It can also be used as a tool for de novo motif discovery or to improve the known motifs based on new data.
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