Gene Regulatory Networks Validation Framework Based in KEGG

In the last few years, DNA microarray technology has attained a very important role in biological and biomedical research. It enables analyzing the relations among thousands of genes simultaneously, generating huge amounts of data. The gene regulatory networks represent, in a graph data structure, genes or gene products and the functional relationships between them. These models have been fully used in Bioinformatics because they provide an easy way to understand gene expression regulation. Nowadays, a lot of gene regulatory network algorithms have been developed as knowledge extraction techniques. A very important task in all these studies is to assure the network models reliability in order to prove that the methods used are precise. This validation process can be carried out by using the inherent information of the input data or by using external biological knowledge. In this last case, these sources of information provide a great opportunity of verifying the biological soundness of the generated networks. In this work, authors present a gene regulatory network validation framework. The proposed approach consists in identifying the biological knowledge included in the input network. To do that, the biochemical pathways information stored in KEGG database will be used.

[1]  Lalji Singh,et al.  Ancient origin and evolution of the Indian wolf: evidence from mitochondrial DNA typing of wolves from Trans-Himalayan region and Pennisular India , 2003, Genome Biology.

[2]  Michael Hecker,et al.  Gene regulatory network inference: Data integration in dynamic models - A review , 2009, Biosyst..

[3]  E. Birney,et al.  Reactome: a knowledgebase of biological pathways , 2004, Nucleic Acids Research.

[4]  Zoubin Ghahramani,et al.  Gene function prediction from synthetic lethality networks via ranking on demand , 2010, Bioinform..

[5]  David R. Bickel,et al.  Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: a case for the second derivative , 2007, Bioinform..

[6]  Susumu Goto,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 2000, Nucleic Acids Res..

[7]  Stefan Bornholdt,et al.  Boolean network models of cellular regulation: prospects and limitations , 2008, Journal of The Royal Society Interface.

[8]  D. Stavenga,et al.  Gyroid cuticular structures in butterfly wing scales: biological photonic crystals , 2007, Journal of The Royal Society Interface.

[9]  Susumu Goto,et al.  KEGG for representation and analysis of molecular networks involving diseases and drugs , 2009, Nucleic Acids Res..

[10]  S. Kauffman.  Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[11]  Andrew J. Bulpitt,et al.  A Primer on Learning in Bayesian Networks for Computational Biology , 2007, PLoS Comput. Biol..

[12]  David R. Bickel,et al.  Gene network inference from high-throughput gene expression data: A plant cell culture case study , 2007 .

[13]  Peer Bork,et al.  KEGG Atlas mapping for global analysis of metabolic pathways , 2008, Nucleic Acids Res..

[14]  Edward R. Dougherty,et al.  Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..

[15]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[16]  Amos Bairoch,et al.  The ENZYME database in 2000 , 2000, Nucleic Acids Res..

[17]  A. Brazma,et al.  Towards reconstruction of gene networks from expression data by supervised learning , 2003, Genome Biology.

[18]  James R. Knight,et al.  A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae , 2000, Nature.

[19]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1999, Innovations in Bayesian Networks.

[20]  Zoubin Ghahramani,et al.  Modeling T-cell activation using gene expression profiling and state-space models , 2004, Bioinform..

[21]  L. Glass,et al.  The logical analysis of continuous, non-linear biochemical control networks. , 1973, Journal of theoretical biology.

[22]  Jesús S. Aguilar-Ruiz,et al.  Inferring gene regression networks with model trees , 2010, BMC Bioinformatics.

[23]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[24]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.