Computational modeling of gene expression from regulatory sequences
type: CreativeWork
description: Regulation of gene expression is an important early step in controlling every biological process that underlies the function of living organisms. Even though gene expression may be regulated in several stages, the modulation occurs mostly at the primary stage known as “transcription”. Teasing out the details of transcriptional regulation is therefore a core focus of biological research. Transcriptional regulation of gene expression is dictated by regulatory DNA sequences, often called cis-regulatory modules (CRM; also known as “enhancer”), that contain specific binding sites for regulatory proteins (transcription factors, TF). The assembly of TFs bound on a CRM drives the desired expression level of the gene associated with the CRM. As the abundance of TFs vary across different cell types, the expression level of the gene, also termed as the “readout” of the CRM, varies accordingly and results in the aforementioned control over biological processes. The rules, collectively known as the “cis-regulatory logic”, to predict gene expression level given information about CRMs and TFs, however, are unclear. Decades of experimental studies have hypothesized mechanisms about parts of this regulatory process (e.g., about the influence of TF-TF interactions), but a comprehensive study of cis-regulatory logic is feasible only through computational models. The subject of this thesis is to develop mechanistic models of gene expression from regulatory sequences and use the models to understand such details of the system that are difficult to assess experimentally. The first part of this thesis develops a model that integrates the regulatory effect of signaling pathways with that of sequence-bound TFs to understand the expression pattern of a gene from its CRM. Given the various types of molecular interactions that the model needs to capture, it is both complex in structure and rich in the number of parameters. Similarly complex models commonly used in other disciplines, from signa…
abstract: Regulation of gene expression is an important early step in controlling every biological process that underlies the function of living organisms. Even though gene expression may be regulated in several stages, the modulation occurs mostly at the primary stage known as “transcription”. Teasing out the details of transcriptional regulation is therefore a core focus of biological research. Transcriptional regulation of gene expression is dictated by regulatory DNA sequences, often called cis-regulatory modules (CRM; also known as “enhancer”), that contain specific binding sites for regulatory proteins (transcription factors, TF). The assembly of TFs bound on a CRM drives the desired expression level of the gene associated with the CRM. As the abundance of TFs vary across different cell types, the expression level of the gene, also termed as the “readout” of the CRM, varies accordingly and results in the aforementioned control over biological processes. The rules, collectively known as the “cis-regulatory logic”, to predict gene expression level given information about CRMs and TFs, however, are unclear. Decades of experimental studies have hypothesized mechanisms about parts of this regulatory process (e.g., about the influence of TF-TF interactions), but a comprehensive study of cis-regulatory logic is feasible only through computational models. The subject of this thesis is to develop mechanistic models of gene expression from regulatory sequences and use the models to understand such details of the system that are difficult to assess experimentally. The first part of this thesis develops a model that integrates the regulatory effect of signaling pathways with that of sequence-bound TFs to understand the expression pattern of a gene from its CRM. Given the various types of molecular interactions that the model needs to capture, it is both complex in structure and rich in the number of parameters. Similarly complex models commonly used in other disciplines, from signa…
author: Samee, Md. Abul Hassan
language: en
keywords: Transcription, Transcriptional regulation, cis-Regulatory sequence, enhancer, Transcription factor, Statistical thermodynamics, Drosophila melanogaster, Computational model, Ensemble model
date published: 2015-07-22T22:17:12Z
date modified: 2026-07-22T22:26:11Z
provider: University of Illinois - Urbana-Champaign
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Illinois at Urbana-Champaign

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2142/78433