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      Identification of key player genes in gene regulatory networks

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          Abstract

          Background

          Identifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. In this work, we reformulate this problem as the optimization problems of computing a Minimum Dominating Set and a Minimum Connected Dominating Set for directed graphs.

          Results

          Both MDS and MCDS are applied to the well-studied gene regulatory networks of the model organisms E. coli and S. cerevisiae and to a pluripotency network for mouse embryonic stem cells. The results show that MCDS can capture most of the known key player genes identified so far in the model organisms. Moreover, this method suggests an additional small set of transcription factors as novel key players for governing the cell-specific gene regulatory network which can also be investigated with regard to diseases. To this aim, we investigated the ability of MCDS to define key drivers in breast cancer. The method identified many known drug targets as members of the MDS and MCDS.

          Conclusions

          This paper proposes a new method to identify key player genes in gene regulatory networks. The Java implementation of the heuristic algorithm explained in this paper is available as a Cytoscape plugin at http://apps.cytoscape.org/apps/mcds. The SageMath programs for solving integer linear programming formulations used in the paper are available at https://github.com/maryamNazarieh/KeyRegulatoryGenesand as supplementary material.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12918-016-0329-5) contains supplementary material, which is available to authorized users.

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          Most cited references25

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Two-component signal transduction.

            Most prokaryotic signal-transduction systems and a few eukaryotic pathways use phosphotransfer schemes involving two conserved components, a histidine protein kinase and a response regulator protein. The histidine protein kinase, which is regulated by environmental stimuli, autophosphorylates at a histidine residue, creating a high-energy phosphoryl group that is subsequently transferred to an aspartate residue in the response regulator protein. Phosphorylation induces a conformational change in the regulatory domain that results in activation of an associated domain that effects the response. The basic scheme is highly adaptable, and numerous variations have provided optimization within specific signaling systems. The domains of two-component proteins are modular and can be integrated into proteins and pathways in a variety of ways, but the core structures and activities are maintained. Thus detailed analyses of a relatively small number of representative proteins provide a foundation for understanding this large family of signaling proteins.
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              Transcriptional regulatory networks in Saccharomyces cerevisiae.

              We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
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                Author and article information

                Contributors
                maryam.nazarieh@bioinformatik.uni-saarland.de
                awiese@mpi-inf.mpg.de
                thorsten.will@bioinformatik.uni-saarland.de
                mhamed@bioinformatik.uni-saarland.de
                volkhard.helms@bioinformatik.uni-saarland.de
                Journal
                BMC Syst Biol
                BMC Syst Biol
                BMC Systems Biology
                BioMed Central (London )
                1752-0509
                6 September 2016
                6 September 2016
                2016
                : 10
                : 1
                : 88
                Affiliations
                [1 ]Center for Bioinformatics, Saarland University, Saarbruecken, Germany
                [2 ]Graduate School of Computer Science, Saarland University, Saarbruecken, Germany
                [3 ]Max Planck Institut fuer Informatik (MPII), Saarbruecken, Germany
                [4 ]Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany
                Author information
                http://orcid.org/0000-0002-2180-9154
                Article
                329
                10.1186/s12918-016-0329-5
                5011974
                27599550
                61ada3ec-d92b-4042-b3fa-448d19141ffd
                © The Author(s) 2016

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 1 July 2016
                : 19 August 2016
                Funding
                Funded by: Graduate School for Computer Science at Saarland University
                Award ID: graduate scholarship
                Award Recipient :
                Funded by: DFG
                Award ID: SFB 1027
                Award Recipient :
                Funded by: DAAD
                Award ID: graduate scholarship
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2016

                Quantitative & Systems biology
                minimum dominating set,minimum connected dominating set,gene regulatory network,integer linear programming,heuristic algorithm

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