Literature DB >> 21386588

Statistical-mechanical lattice models for protein-DNA binding in chromatin.

Vladimir B Teif1, Karsten Rippe.   

Abstract

Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibria measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical-mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.
© 2010 IOP Publishing Ltd

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Year:  2010        PMID: 21386588     DOI: 10.1088/0953-8984/22/41/414105

Source DB:  PubMed          Journal:  J Phys Condens Matter        ISSN: 0953-8984            Impact factor:   2.333


  14 in total

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5.  Reverse engineering gene regulatory networks from measurement with missing values.

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Journal:  EURASIP J Bioinform Syst Biol       Date:  2017-01-10

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7.  Genome-wide analysis of H4K5 acetylation associated with fear memory in mice.

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8.  Regulation of the nucleosome repeat length in vivo by the DNA sequence, protein concentrations and long-range interactions.

Authors:  Daria A Beshnova; Andrey G Cherstvy; Yevhen Vainshtein; Vladimir B Teif
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9.  Genomic Targets and Features of BarA-UvrY (-SirA) Signal Transduction Systems.

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10.  Removing Background Co-occurrences of Transcription Factor Binding Sites Greatly Improves the Prediction of Specific Transcription Factor Cooperations.

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Journal:  Front Genet       Date:  2018-05-29       Impact factor: 4.599

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