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dc.contributor.advisorLin, Kevinen
dc.contributor.authorThornton, Vera Elizabeth
dc.creatorThornton, Vera Elizabethen
dc.date.accessioned2017-08-10T18:01:56Z
dc.date.available2017-08-10T18:01:56Z
dc.date.issued2017
dc.identifier.citationThornton, Vera Elizabeth. (2017). An Investigation of In-Silico Evolving Networks (Bachelor's thesis, University of Arizona, Tucson, USA).
dc.identifier.urihttp://hdl.handle.net/10150/625226
dc.description.abstractThis honors thesis project builds on the work of Paul Francois, Eric Siggia, and Vincent Hakim that uses in-silico genetic network models to study evolution. Genetic networks are represented as systems of ordinary differential equations (ODEs). Each term in the differential equation corresponds to a chemical reaction, and the solution of the ODE, found using the fourth order Runge-Kutta algorithm, yields the levels of proteins in the system over time which is due to the expression of genes in the network. After replicating specific networks, a more general program was written which began with a very simple network and evolved to a more complex system through successive rounds of mutation and selection. Selection was achieved by solving for the levels of protein over time, and then scoring the network based on how closely these levels approached the desired behavior profile. A genetic network model similar to one presented in the original papers was successfully evolved. Then the same program was used to attempt to evolve an oscillating network system.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleAn Investigation of In-Silico Evolving Networksen_US
dc.typetexten
dc.typeElectronic Thesisen
thesis.degree.grantorUniversity of Arizonaen
thesis.degree.levelbachelorsen
thesis.degree.disciplineHonors Collegeen
thesis.degree.disciplineMathematicsen
thesis.degree.nameB.S.en
refterms.dateFOA2018-06-14T12:34:34Z
html.description.abstractThis honors thesis project builds on the work of Paul Francois, Eric Siggia, and Vincent Hakim that uses in-silico genetic network models to study evolution. Genetic networks are represented as systems of ordinary differential equations (ODEs). Each term in the differential equation corresponds to a chemical reaction, and the solution of the ODE, found using the fourth order Runge-Kutta algorithm, yields the levels of proteins in the system over time which is due to the expression of genes in the network. After replicating specific networks, a more general program was written which began with a very simple network and evolved to a more complex system through successive rounds of mutation and selection. Selection was achieved by solving for the levels of protein over time, and then scoring the network based on how closely these levels approached the desired behavior profile. A genetic network model similar to one presented in the original papers was successfully evolved. Then the same program was used to attempt to evolve an oscillating network system.


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