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Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp01x920g0539
Title: Mathematical Models for Understanding Dynamic Cellular Systems
Authors: Mattingly, Henry Hughes
Advisors: Shvartsman, Stanislav Y
Kevrekidis, Ioannis G
Contributors: Chemical and Biological Engineering Department
Subjects: Bioengineering
Chemical engineering
Issue Date: 2017
Publisher: Princeton, NJ : Princeton University
Abstract: Cellular processes, such as fate decisions and mitotic divisions, are dynamic. Complex behaviors emerge from networks of interactions among numerous components: time-varying extracellular signals induce expression of specific genes, and periodic changes in intracellular molecule concentrations coordinate mitotic entry and exit. Quantitative understanding of these processes requires mathematical models. Models can make predictions about system dynamics in conditions that are difficult to probe experimentally, explain how systems-level behaviors emerge from a network of interactions, and convert observed data into constraints on future behaviors. This thesis uses mathematical modeling in each of these ways to learn about various dynamical biological phenomena. In the first chapter, models are used to estimate time-varying signals controlling meiosis that are difficult to measure experimentally. In the second, a simple model of the embryonic cell cycle is used to understand how robust oscillations arise in that system. And in the third and ongoing chapter, a model is used to explore how much we can expect learn about a biochemical mechanism from planned experiments.
URI: http://arks.princeton.edu/ark:/88435/dsp01x920g0539
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog: catalog.princeton.edu
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Chemical and Biological Engineering

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