Modeling Genetic Networks: Comparison of Static and Dynamic Models

Biomedical research has been revolutionized by high-throughput techniques and the enormous amount of biological data they are able to generate. The interest shown over network models and systems biology is rapidly raising. Genetic networks arise as an essential task to mine these data since they explain the function of genes in terms of how they influence other genes. Many modeling approaches have been proposed for building genetic networks up. However, it is not clear what the advantages and disadvantages of each model are. There are several ways to discriminate network building models, being one of the most important whether the data being mined presents a static or dynamic fashion. In this work we compare static and dynamic models over a problem related to the inflammation and the host response to injury. We show how both models provide complementary information and cross-validate the obtained results.

[1]  M. Reinders,et al.  Genetic network modeling. , 2002, Pharmacogenomics.

[2]  John D. Storey,et al.  A network-based analysis of systemic inflammation in humans , 2005, Nature.

[3]  Oscar Cordón,et al.  Optimal Selection of Microarray Analysis Methods Using a Conceptual Clustering Algorithm , 2006, EvoWorkshops.

[4]  Gregory W. Carter,et al.  Inferring network interactions within a cell , 2005, Briefings Bioinform..

[5]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Korbinian Strimmer.  INFERRING GENE DEPENDENCY NETWORKS FROM GENOMIC LONGITUDINAL DATA : A FUNCTIONAL DATA APPROACH , 2006 .

[7]  Vasant Honavar,et al.  Temporal Boolean Network Models of Genetic Networks and their Inference from Gene Expression Time Series , 2001, Complex Syst..

[8]  Oscar Cordón,et al.  Mining Structural Databases: An Evolutionary Multi-Objetive Conceptual Clustering Methodology , 2006, EvoWorkshops.

[9]  Debashis Ghosh,et al.  Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer , 2003, Functional & Integrative Genomics.

[10]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[11]  John J. Rice,et al.  Making the most of it: pathway reconstruction and integrative simulation using the data at hand , 2004 .

[12]  A. Arkin,et al.  Simulation of prokaryotic genetic circuits. , 1998, Annual review of biophysics and biomolecular structure.

[13]  C. Ouzounis,et al.  Expansion of the BioCyc collection of pathway/genome databases to 160 genomes , 2005, Nucleic acids research.

[14]  I. Simon,et al.  Combined static and dynamic analysis for determining the quality of time-series expression profiles , 2005, Nature Biotechnology.

[15]  Alain Bellido,et al.  Spatial genetic pattern in the land mollusc Helix aspersa inferred from a 'centre-based clustering' procedure. , 2006, Genetical research.

[16]  Bradley Efron,et al.  Local False Discovery Rates , 2005 .

[17]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[18]  H Kishino,et al.  Correspondence analysis of genes and tissue types and finding genetic links from microarray data. , 2000, Genome informatics. Workshop on Genome Informatics.

[19]  Korbinian Strimmer,et al.  An empirical Bayes approach to inferring large-scale gene association networks , 2005, Bioinform..

[20]  Sean R. Eddy,et al.  Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids , 1998 .

[21]  Susumu Goto,et al.  The KEGG resource for deciphering the genome , 2004, Nucleic Acids Res..

[22]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..