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Elastic Network Models in Biology: From Protein Mode Spectra to Chromatin Dynamics

Zhang, She (2020) Elastic Network Models in Biology: From Protein Mode Spectra to Chromatin Dynamics. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Biomacromolecules perform their functions by accessing conformations energetically favored by their structure-encoded equilibrium dynamics. Elastic network model (ENM) analysis has been widely used to decompose the equilibrium dynamics of a given molecule into a spectrum of modes of motions, which separates robust, global motions from local fluctuations. The scalability and flexibility of the ENMs permit us to efficiently analyze the spectral dynamics of large systems or perform comparative analysis for large datasets of structures. I showed in this thesis how ENMs can be adapted (1) to analyze protein superfamilies that share similar tertiary structures but may differ in their sequence and functional dynamics, and (2) to analyze chromatin dynamics using contact data from Hi-C experiments, and (3) to perform a comparative analysis of genome topology across different types of cell lines. The first study showed that protein family members share conserved, highly cooperative (global) modes of motion. A low-to-intermediate frequency spectral regime was shown to have a maximal impact on the functional differentiation of families into subfamilies. The second study demonstrated the Gaussian Network Model (GNM) can accurately model chromosomal mobility and couplings between genomic loci at multiple scales: it can quantify the spatial fluctuations in the positions of gene loci, detect large genomic compartments and smaller topologically-associating domains (TADs) that undergo en bloc movements, and identify dynamically coupled distal regions along the chromosomes. The third study revealed close similarities between chromosomal dynamics across different cell lines on a global scale, but notable cell-specific variations in the spatial fluctuations of genomic loci. It also called attention to the role of the intrinsic spatial dynamics of chromatin as a determinant of cell differentiation. Together, these studies provide a comprehensive view of the versatility and utility of the ENMs in analyzing spatial dynamics of biomolecules, from individual proteins to the entire chromatin.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Zhang, She
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCarvunis, Anne-Ruxandraanc201@pitt.eduanc2010000-0002-6474-6413
Thesis AdvisorBahar, Ivetbahar@pitt.edubahar0000-0001-9959-4176
Committee MemberKingsford,
Committee MemberWhitcomb, Davidwhitcomb@pitt.eduwhitcomb
Date: 15 June 2020
Date Type: Publication
Defense Date: 24 April 2020
Approval Date: 15 June 2020
Submission Date: 5 May 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 186
Institution: University of Pittsburgh
Schools and Programs: School of Medicine > Computational Biology
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Elastic network model, Chromatin dynamics, Hi-C, Protein family, Normal mode analysis, Biological network, Topological associating domain, compartment
Date Deposited: 16 Jun 2020 01:52
Last Modified: 16 Jun 2020 01:52

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