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dc.contributor.advisorJeremy England.en_US
dc.contributor.authorPerunov, Nikolayen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Physics.en_US
dc.date.accessioned2016-06-22T17:50:03Z
dc.date.available2016-06-22T17:50:03Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/103232
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Department of Physics, 2016.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 119-128).en_US
dc.description.abstractIn this thesis, we use the methods of statistical physics to provide quantitative insights into the behavior of biological systems. In the first half of the thesis, we use equilibrium statistical physics to develop a phenomenological model of how the hydrophobic effect impacts the structure of proteins, and in the second half, we study the phenomenon of adaptation and Darwinian selection from the standpoint of nonequilibrium statistical physics. It has been known for a long time that the hydrophobic effect plays a major role in driving protein folding. However, it has been challenging to translate this understanding into a predictive, quantitative theory of how the full pattern of sequence hydrophobicity in a protein helps to determine its structure. Here, we develop and apply a phenomenological theory of the sequence-structure relationship in globular protein domains. In an effort to optimize parameters for the model, we first analyze the patterns of backbone burial found in single-domain crystal structures and discover that classic hydrophobicity scales derived from bulk physicochemical properties of amino acids are already nearly optimal for prediction of burial using the model. Subsequently, we apply the model to studying structural fluctuations in proteins and establish a means of identifying ligand-binding and protein-protein interaction sites using this approach. In the second half of the thesis, we undertake to address the question of adaptation from the standpoint of physics. Building on past fundamental results in nonequilibrium statistical mechanics, we demonstrate a generalization of the Helmholtz free energy for the finite-time stochastic evolution of driven Newtonian matter. By analyzing this expression, we show a general tendency in a broad class of driven many-particle systems toward self-organization into states formed through reliable absorption and dissipation of work energy from the surrounding environment. We demonstrate how this tendency plays out in the familiar example of Darwinian competition between two exponentially growing self-replicators. Subsequently, we illustrate the more general mechanism by which extra dissipation drives adaptation by analyzing the process of random hopping in driven energy landscapes.en_US
dc.description.statementofresponsibilityby Nikolay Perunov.en_US
dc.format.extent128 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectPhysics.en_US
dc.titleStatistical physics of biological self-organization : case studies in protein folding and adaptation to time-varying fieldsen_US
dc.title.alternativeCase studies in protein folding and adaptation to time-varying fieldsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Physics
dc.identifier.oclc951540093en_US


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