Literature DB >> 22585140

How little do we actually know? On the size of gene regulatory networks.

Richard Röttger1, Ulrich Rückert, Jan Taubert, Jan Baumbach.   

Abstract

The National Center for Biotechnology Information (NCBI) recently announced the availability of whole genome sequences for more than 1,000 species. And the number of sequenced individual organisms is growing. Ongoing improvement of DNA sequencing technology will further contribute to this, enabling large-scale evolution and population genetics studies. However, the availability of sequence information is only the first step in understanding how cells survive, reproduce, and adjust their behavior. The genetic control behind organized development and adaptation of complex organisms still remains widely undetermined. One major molecular control mechanism is transcriptional gene regulation. The direct juxtaposition of the total number of sequenced species to the handful of model organisms with known regulations is surprising. Here, we investigate how little we even know about these model organisms. We aim to predict the sizes of the whole-organism regulatory networks of seven species. In particular, we provide statistical lower bounds for the expected number of regulations. For Escherichia coli we estimate at most 37 percent of the expected gene regulatory interactions to be already discovered, 24 percent for Bacillus subtilis, and <3% human, respectively. We conclude that even for our best researched model organisms we still lack substantial understanding of fundamental molecular control mechanisms, at least on a large scale.

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Year:  2012        PMID: 22585140     DOI: 10.1109/TCBB.2012.71

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  11 in total

1.  ASP-G: an ASP-based method for finding attractors in genetic regulatory networks.

Authors:  Mushthofa Mushthofa; Gustavo Torres; Yves Van de Peer; Kathleen Marchal; Martine De Cock
Journal:  Bioinformatics       Date:  2014-07-15       Impact factor: 6.937

2.  CMRegNet-An interspecies reference database for corynebacterial and mycobacterial regulatory networks.

Authors:  Vinicius A C Abreu; Sintia Almeida; Sandeep Tiwari; Syed Shah Hassan; Diego Mariano; Artur Silva; Jan Baumbach; Vasco Azevedo; Richard Röttger
Journal:  BMC Genomics       Date:  2015-06-11       Impact factor: 3.969

3.  SporeWeb: an interactive journey through the complete sporulation cycle of Bacillus subtilis.

Authors:  Robyn T Eijlander; Anne de Jong; Antonina O Krawczyk; Siger Holsappel; Oscar P Kuipers
Journal:  Nucleic Acids Res       Date:  2013-10-28       Impact factor: 16.971

4.  KeyPathwayMinerWeb: online multi-omics network enrichment.

Authors:  Markus List; Nicolas Alcaraz; Martin Dissing-Hansen; Henrik J Ditzel; Jan Mollenhauer; Jan Baumbach
Journal:  Nucleic Acids Res       Date:  2016-05-05       Impact factor: 16.971

5.  Graphlet-based Characterization of Directed Networks.

Authors:  Anida Sarajlić; Noël Malod-Dognin; Ömer Nebil Yaveroğlu; Nataša Pržulj
Journal:  Sci Rep       Date:  2016-10-13       Impact factor: 4.379

6.  PRODORIC2: the bacterial gene regulation database in 2018.

Authors:  Denitsa Eckweiler; Christian-Alexander Dudek; Juliane Hartlich; David Brötje; Dieter Jahn
Journal:  Nucleic Acids Res       Date:  2018-01-04       Impact factor: 16.971

7.  Evolutionary constraints on the complexity of genetic regulatory networks allow predictions of the total number of genetic interactions.

Authors:  Adrian I Campos; Julio A Freyre-González
Journal:  Sci Rep       Date:  2019-03-06       Impact factor: 4.379

8.  Counting motifs in the human interactome.

Authors:  Ngoc Hieu Tran; Kwok Pui Choi; Louxin Zhang
Journal:  Nat Commun       Date:  2013       Impact factor: 14.919

9.  An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection.

Authors:  Linlin Xing; Maozu Guo; Xiaoyan Liu; Chunyu Wang; Lei Wang; Yin Zhang
Journal:  BMC Genomics       Date:  2017-11-17       Impact factor: 3.969

10.  A comprehensive evaluation of module detection methods for gene expression data.

Authors:  Wouter Saelens; Robrecht Cannoodt; Yvan Saeys
Journal:  Nat Commun       Date:  2018-03-15       Impact factor: 14.919

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