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      GRiP: a computational tool to simulate transcription factor binding in prokaryotes

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      1 , 2 , * , 1 , 2
      Bioinformatics
      Oxford University Press

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          Abstract

          Motivation: Transcription factors (TFs) are proteins that regulate gene activity by binding to specific sites on the DNA. Understanding the way these molecules locate their target site is of great importance in understanding gene regulation. We developed a comprehensive computational model of this process and estimated the model parameters in (N.R.Zabet and B.Adryan, submitted for publication).

          Results: GRiP (gene regulation in prokaryotes) is a highly versatile implementation of this model and simulates the search process in a computationally efficient way. This program aims to provide researchers in the field with a flexible and highly customizable simulation framework. Its features include representation of DNA sequence, TFs and the interaction between TFs and the DNA (facilitated diffusion mechanism), or between various TFs (cooperative behaviour). The software will record both information on the dynamics associated with the search process (locations of molecules) and also steady-state results (affinity landscape, occupancy-bias and collision hotspots).

          Availability: http://logic.sysbiol.cam.ac.uk/grip, program and source code

          Contact: n.r.zabet@ 123456gen.cam.ac.uk

          Supplementary information: Supplementary data are available at Bioinformatics online.

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          Most cited references13

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          Exact stochastic simulation of coupled chemical reactions

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            DNA binding sites: representation and discovery.

            G Stormo (2000)
            The purpose of this article is to provide a brief history of the development and application of computer algorithms for the analysis and prediction of DNA binding sites. This problem can be conveniently divided into two subproblems. The first is, given a collection of known binding sites, develop a representation of those sites that can be used to search new sequences and reliably predict where additional binding sites occur. The second is, given a set of sequences known to contain binding sites for a common factor, but not knowing where the sites are, discover the location of the sites in each sequence and a representation for the specificity of the protein.
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              Probing transcription factor dynamics at the single-molecule level in a living cell.

              Transcription factors regulate gene expression through their binding to DNA. In a living Escherichia coli cell, we directly observed specific binding of a lac repressor, labeled with a fluorescent protein, to a chromosomal lac operator. Using single-molecule detection techniques, we measured the kinetics of binding and dissociation of the repressor in response to metabolic signals. Furthermore, we characterized the nonspecific binding to DNA, one-dimensional (1D) diffusion along DNA segments, and 3D translocation among segments through cytoplasm at the single-molecule level. In searching for the operator, a lac repressor spends approximately 90% of time nonspecifically bound to and diffusing along DNA with a residence time of <5 milliseconds. The methods and findings can be generalized to other nucleic acid binding proteins.
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                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                bioinfo
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                1 May 2012
                16 March 2012
                16 March 2012
                : 28
                : 9
                : 1287-1289
                Affiliations
                1Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR and 2Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK
                Author notes
                * To whom correspondence should be addressed.

                Associate Editor: Trey Ideker

                Article
                bts132
                10.1093/bioinformatics/bts132
                3338021
                22426343
                c4925858-64ec-435c-b5ef-697be8b1d850
                © The Author(s) 2012. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 November 2011
                : 2 February 2012
                : 12 March 2012
                Page count
                Pages: 3
                Categories
                Applications Note
                Systems Biology

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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