Multiscale Modeling Of Cell Fate Switching To Predict Patient-Specific Response To Combination Cancer Therapy
type: CreativeWork
description: All cells in the human body share the same DNA sequence, but differ in their functional identity, guided by a wide array of regulatory mechanisms controlling cellular lineage commitment and encoded in the unique epigenome of each cell type. Recent experimental studies with induced pluripotent stem cells have allowed researchers to investigate the dynamic nature of cell identity and relationships between gene regulation and differentiation. These studies have major implications for our understanding of not only human development, but also disease as cancers, and some neurological diseases, arise in part due to inappropriate persistence of cells in immature differentiation states. These studies have proliferated massive multi-omics databases as next generation sequencing (NGS) technologies are applied to extensively profile stem cells, in vitro differentiated cell populations, and cancer patient cohorts. As these data accumulate, important unanswered questions remain, including to what extent do physical states of genes change in development and disease, and how do these changes meaningful alter cell signaling pathways and clinically impact individual patients. To address these questions, new computational tools are needed to 1) rigorously assess epigenomic state changes captured with NGS modalities in the course of lineage specification, and to 2) integrate models of patient (epi-)genomic states with those of disease-associated cell signaling pathways and clinical outcomes. This thesis describes the development and applications of computational methods to help address these needs. First described is a statistical tool for classifying long-range looping interactions that change across developmental models and disease states from data captured with NGS technologies. Its application is demonstrated for study of chromatin looping state changes in the course of neural lineage commitment and neuronal activation. Then a multiscale framework is described that integrates pati…
abstract: All cells in the human body share the same DNA sequence, but differ in their functional identity, guided by a wide array of regulatory mechanisms controlling cellular lineage commitment and encoded in the unique epigenome of each cell type. Recent experimental studies with induced pluripotent stem cells have allowed researchers to investigate the dynamic nature of cell identity and relationships between gene regulation and differentiation. These studies have major implications for our understanding of not only human development, but also disease as cancers, and some neurological diseases, arise in part due to inappropriate persistence of cells in immature differentiation states. These studies have proliferated massive multi-omics databases as next generation sequencing (NGS) technologies are applied to extensively profile stem cells, in vitro differentiated cell populations, and cancer patient cohorts. As these data accumulate, important unanswered questions remain, including to what extent do physical states of genes change in development and disease, and how do these changes meaningful alter cell signaling pathways and clinically impact individual patients. To address these questions, new computational tools are needed to 1) rigorously assess epigenomic state changes captured with NGS modalities in the course of lineage specification, and to 2) integrate models of patient (epi-)genomic states with those of disease-associated cell signaling pathways and clinical outcomes. This thesis describes the development and applications of computational methods to help address these needs. First described is a statistical tool for classifying long-range looping interactions that change across developmental models and disease states from data captured with NGS technologies. Its application is demonstrated for study of chromatin looping state changes in the course of neural lineage commitment and neuronal activation. Then a multiscale framework is described that integrates pati…
author: Fernandez, Lindsey
language: en
date published: 2021
date modified: 2026-07-24T03:45:15Z
provider: University of Pennsylvania
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Pennsylvania