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dc.contributor.advisor Hartemink, Alexander J en_US
dc.contributor.author Wasson, Todd Steven en_US
dc.date.accessioned 2011-01-06T16:03:58Z
dc.date.available 2012-01-01T05:30:13Z
dc.date.issued 2010 en_US
dc.identifier.uri http://hdl.handle.net/10161/3139
dc.description Dissertation en_US
dc.description.abstract <p>Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not.</p><p>Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an 'occupancy profile', a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding.</p><p>We then develop a statistical framework for tuning our model concentrations to further improve its predictions. Importantly, this tuning optimizes with respect to actual biological data. We take steps to ensure that our tuned parameters are biologically plausible.</p><p>Finally, we discuss novel extensions and applications of our model, suggesting next steps in its development and deployment.</p> en_US
dc.subject Biology, Bioinformatics en_US
dc.subject Computer Science en_US
dc.subject Statistics en_US
dc.subject Boltzmann chains en_US
dc.subject Computational Biology en_US
dc.subject DNA binding en_US
dc.subject Hidden Markov models en_US
dc.subject Statistical mechanics en_US
dc.subject Transcription factors en_US
dc.title Modeling Multi-factor Binding of the Genome en_US
dc.type Dissertation en_US
dc.department Computational Biology and Bioinformatics en_US
duke.embargo.months 12 en_US

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