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Modeling bio-networks at multiple scales
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Title
Modeling bio-networks at multiple scales
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McQuade
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Sean T.
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Sean McQuade
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author
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Piccoli
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Benedetto
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Benedetto Piccoli
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Advisory Committee
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Jongmin
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Jongmin Nam
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Advisory Committee
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internal member
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Nerurkar
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Mahesh
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Mahesh Nerurkar
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Advisory Committee
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internal member
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Azer
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Karim
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Karim Azer
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Advisory Committee
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Rutgers University
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Camden Graduate School
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theses
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2019
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2019-05
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2019
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English
Abstract
A network facilitates the description of selective interactions among the variables of a system. In this work, networks are used to depict selective interactions between molecules, cells, and agents. This research leverages the structure of a network to model biological systems using Ordinary Differential Equations.
The first bio-network investigated is a metabolic network (the nano scale). Metabolism can be captured as a directed graph of nodes and edges. The nodes represent biomolecules or metabolites, and each edge corresponds to a chemical reaction in which the nodes at the tail of the edge are reactants and nodes at the head of the edge are products. The goal is to develop a methodology to accurately simulate large networks. This methodology has been named Linear-In-Flux Expressions (briefly LIFE).
The second class of bio-network investigated is a cell lineage (the micro scale). This is a network of daughter cells from an embryo. The goal is to develop an analytical procedure which can be used on data regarding potential cis-regulatory modules(briefly CRMs) to determine which are active CRMs, as well as where (which cell in the organism) and when (which cell generation) a CRM was active. From this analysis we predict how perturbations of spatial activity will impact the data, and confirm predictions with simulation.
The third class of bio-network investigated is a collection of interacting agents (the macro scale). In opinion formation models, these agents often represent a multidimensional opinion held by an intelligent organism. The goal is to model the evolution of opinions over time as they are influenced by other opinions. Models of this type study the emergence of global patterns driven by local interactions. This work on opinion formation models has two aims: 1. construct a mathematical framework to define classical opinion formation models on the more complex state space of a general compact Riemannian manifold, and 2. investigate the effects of dynamics which govern how influential each agent is.
The first bio-network investigated is a metabolic network (the nano scale). Metabolism can be captured as a directed graph of nodes and edges. The nodes represent biomolecules or metabolites, and each edge corresponds to a chemical reaction in which the nodes at the tail of the edge are reactants and nodes at the head of the edge are products. The goal is to develop a methodology to accurately simulate large networks. This methodology has been named Linear-In-Flux Expressions (briefly LIFE).
The second class of bio-network investigated is a cell lineage (the micro scale). This is a network of daughter cells from an embryo. The goal is to develop an analytical procedure which can be used on data regarding potential cis-regulatory modules(briefly CRMs) to determine which are active CRMs, as well as where (which cell in the organism) and when (which cell generation) a CRM was active. From this analysis we predict how perturbations of spatial activity will impact the data, and confirm predictions with simulation.
The third class of bio-network investigated is a collection of interacting agents (the macro scale). In opinion formation models, these agents often represent a multidimensional opinion held by an intelligent organism. The goal is to model the evolution of opinions over time as they are influenced by other opinions. Models of this type study the emergence of global patterns driven by local interactions. This work on opinion formation models has two aims: 1. construct a mathematical framework to define classical opinion formation models on the more complex state space of a general compact Riemannian manifold, and 2. investigate the effects of dynamics which govern how influential each agent is.
Subject
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Topic
Systems biology
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Computational and Integrative Biology
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Rutgers University Electronic Theses and Dissertations
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ETD_9968
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1 online resource (vii, 171 pages)
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Ph.D.
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Includes bibliographical references
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Computational biology
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Camden Graduate School Electronic Theses and Dissertations
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rucore10005600001
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doi:10.7282/t3-1d8f-0879
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ETD doctoral
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Rights
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The author owns the copyright to this work.
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Name
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McQuade
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Sean
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T.
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Permission or license
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2019-05-03 12:40:25
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Sean T. McQuade
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Rutgers University. Camden Graduate School
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Copyright protected
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Open
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