Deciphering modular and dynamic behaviors of transcriptional networks

The coordinated and dynamic modulation or interaction of genes or proteins acts as an important mechanism used by a cell in functional regulation. Recent studies have shown that many transcriptional networks exhibit a scale-free topology and hierarchical modular architecture. It has also been shown that transcriptional networks or pathways are dynamic and behave only in certain ways and controlled manners in response to disease development, changing cellular conditions, and different environmental factors. Moreover, evolutionarily conserved and divergent transcriptional modules underline fundamental and species-specific molecular mechanisms controlling disease development or cellular phenotypes. Various computational algorithms have been developed to explore transcriptional networks and modules from gene expression data. In silico studies have also been made to mimic the dynamic behavior of regulatory networks, analyzing how disease or cellular phenotypes arise from the connectivity or networks of genes and their products. Here, we review the recent development in computational biology research on deciphering modular and dynamic behaviors of transcriptional networks, highlighting important findings. We also demonstrate how these computational algorithms can be applied in systems biology studies as on disease, stem cells, and drug discovery.

[1]  H. Sebastian Seung,et al.  Learning the parts of objects by non-negative matrix factorization , 1999, Nature.

[2]  Nicola J. Rinaldi,et al.  Computational discovery of gene modules and regulatory networks , 2003, Nature Biotechnology.

[3]  César A. Hidalgo,et al.  Scale-free networks , 2008, Scholarpedia.

[4]  J. Hopfield,et al.  From molecular to modular cell biology , 1999, Nature.

[5]  Brendan J. Frey,et al.  Multi-way clustering of microarray data using probabilistic sparse matrix factorization , 2005, ISMB.

[6]  W. Wong,et al.  Functional annotation and network reconstruction through cross-platform integration of microarray data , 2005, Nature Biotechnology.

[7]  Satoru Miyano,et al.  Use of Gene Networks for Identifying and Validating Drug Targets , 2003, J. Bioinform. Comput. Biol..

[8]  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.

[9]  S. Bergmann,et al.  Similarities and Differences in Genome-Wide Expression Data of Six Organisms , 2003, PLoS biology.

[10]  Markus J. Herrgård,et al.  Reconciling gene expression data with known genome-scale regulatory network structures. , 2003, Genome research.

[11]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[12]  D. Koller,et al.  A module map showing conditional activity of expression modules in cancer , 2004, Nature Genetics.

[13]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[14]  Adam A. Margolin,et al.  Reverse engineering of regulatory networks in human B cells , 2005, Nature Genetics.

[15]  Roded Sharan,et al.  Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Homin K. Lee,et al.  Coexpression analysis of human genes across many microarray data sets. , 2004, Genome research.

[17]  S. Batzoglou,et al.  Application of independent component analysis to microarrays , 2003, Genome Biology.

[18]  S. Horvath,et al.  Statistical Applications in Genetics and Molecular Biology , 2011 .

[19]  David Lindgren,et al.  Independent component analysis reveals new and biologically significant structures in micro array data , 2006, BMC Bioinformatics.

[20]  E. Dougherty,et al.  Gene perturbation and intervention in probabilistic Boolean networks. , 2002, Bioinformatics.

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

[22]  Edward R. Dougherty,et al.  Coefficient of determination in nonlinear signal processing , 2000, Signal Process..

[23]  S. Bergmann,et al.  Comparative Gene Expression Analysis by a Differential Clustering Approach: Application to the Candida albicans Transcription Program , 2005, PLoS genetics.

[24]  Satoru Miyano,et al.  Estimation of Genetic Networks and Functional Structures Between Genes by Using Bayesian Networks and Nonparametric Regression , 2001, Pacific Symposium on Biocomputing.

[25]  M. Gerstein,et al.  Genomic analysis of regulatory network dynamics reveals large topological changes , 2004, Nature.

[26]  David Eisenberg,et al.  Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles , 2001, Nature Genetics.

[27]  D. Botstein,et al.  Singular value decomposition for genome-wide expression data processing and modeling. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Ker-Chau Li,et al.  A system for enhancing genome-wide coexpression dynamics study. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[29]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[30]  Xiaobo Zhou,et al.  Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor design , 2003, Signal Process..

[31]  Yu Sun,et al.  The discovery of transcriptional modules by a two-stage matrix decomposition approach , 2007, Bioinform..

[32]  T. Ideker,et al.  A new approach to decoding life: systems biology. , 2001, Annual review of genomics and human genetics.

[33]  G. Gibson,et al.  Cross-species comparison of genome-wide expression patterns , 2004, Genome Biology.

[34]  Neal S. Holter,et al.  Dynamic modeling of gene expression data. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[35]  K. Calame,et al.  Signaling Pathways Activated by Oncogenic Forms of Abl Tyrosine Kinase* , 1999, The Journal of Biological Chemistry.

[36]  Piero Carninci,et al.  Transcriptional network dynamics in macrophage activation. , 2006, Genomics.

[37]  Pablo Tamayo,et al.  Metagenes and molecular pattern discovery using matrix factorization , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[38]  B. Snel,et al.  The yeast coexpression network has a small‐world, scale‐free architecture and can be explained by a simple model , 2004, EMBO reports.

[39]  Alexander J. Hartemink,et al.  Informative Structure Priors: Joint Learning of Dynamic Regulatory Networks from Multiple Types of Data , 2004, Pacific Symposium on Biocomputing.

[40]  Huai Li,et al.  Systematic intervention of transcription for identifying network response to disease and cellular phenotypes , 2006, Bioinform..

[41]  Guoli Wang,et al.  LS-NMF: A modified non-negative matrix factorization algorithm utilizing uncertainty estimates , 2006, BMC Bioinformatics.

[42]  Philip M. Kim,et al.  Subsystem identification through dimensionality reduction of large-scale gene expression data. , 2003, Genome research.

[43]  Chiara Sabatti,et al.  Network component analysis: Reconstruction of regulatory signals in biological systems , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[44]  E. Davidson.  Genomic Regulatory Systems: Development and Evolution , 2005 .

[45]  Huai Li,et al.  Analysis of Gene Coexpression by B-Spline Based CoD Estimation , 2007, EURASIP J. Bioinform. Syst. Biol..

[46]  S. Horvath,et al.  Conservation and evolution of gene coexpression networks in human and chimpanzee brains , 2006, Proceedings of the National Academy of Sciences.

[47]  J. Collado-Vides,et al.  Modular analysis of the transcriptional regulatory network of E. coli. , 2005, Trends in genetics : TIG.

[48]  Yuan Qi,et al.  Modularity and Dynamics of Cellular Networks , 2006, PLoS Comput. Biol..

[49]  Michael Griffin,et al.  Gene co-expression network topology provides a framework for molecular characterization of cellular state , 2004, Bioinform..

[50]  Michael L. Bittner,et al.  Growing genetic regulatory networks from seed genes , 2004, Bioinform..

[51]  S. Muta,et al.  Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades. , 2003, DNA research : an international journal for rapid publication of reports on genes and genomes.

[52]  Ying Liu,et al.  Cross-species transcriptional profiles establish a functional portrait of embryonic stem cells. , 2007, Genomics.

[53]  Yaniv Ziv,et al.  Revealing modular organization in the yeast transcriptional network , 2002, Nature Genetics.

[54]  Yuan Gao,et al.  Improving molecular cancer class discovery through sparse non-negative matrix factorization , 2005 .

[55]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

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

[57]  C. Sawyers,et al.  Activity of a specific inhibitor of the BCR-ABL tyrosine kinase in the blast crisis of chronic myeloid leukemia and acute lymphoblastic leukemia with the Philadelphia chromosome. , 2001, The New England journal of medicine.

[58]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[59]  Hsinchun Chen,et al.  A framework of integrating gene relations from heterogeneous data sources: an experiment on Arabidopsis thaliana , 2006, Bioinform..

[60]  Wolfram Liebermeister,et al.  Linear modes of gene expression determined by independent component analysis , 2002, Bioinform..

[61]  Sven Bergmann,et al.  Defining transcription modules using large-scale gene expression data , 2004, Bioinform..

[62]  Edward R. Dougherty,et al.  CAN MARKOV CHAIN MODELS MIMIC BIOLOGICAL REGULATION , 2002 .