Improving Gene Network Inference by Comparing Expression Time-series across Species, Developmental Stages or Tissues

We present a method for gene network inference and revision based on time-series data. Gene networks are modeled using linear differential equations and a generalized stepwise multiple linear regression procedure is used to recover the interaction coefficients. Our system is designed for the recovery of gene interactions concurrently in many gene regulatory networks related by a tree or a more general graph. We show how this comparative framework can facilitate the recovery of the networks and improve the quality of the solutions inferred.

[1]  David Page,et al.  Modelling regulatory pathways in E. coli from time series expression profiles , 2002, ISMB.

[2]  Mattias Wahde,et al.  Effective dimensionality of large-scale expression data using principal component analysis. , 2002, Bio Systems.

[3]  Neal S. Holter,et al.  Fundamental patterns underlying gene expression profiles: simplicity from complexity. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Satoru Miyano,et al.  Identification of Genetic Networks from a Small Number of Gene Expression Patterns Under the Boolean Network Model , 1998, Pacific Symposium on Biocomputing.

[5]  Marcel J. T. Reinders,et al.  Genetic network models: a comparative study , 2001, SPIE BiOS.

[6]  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.

[7]  P. Brown,et al.  Exploring the metabolic and genetic control of gene expression on a genomic scale. , 1997, Science.

[8]  E. P. van Someren.  Searching for Limited Connectivity in Genetic Network Models , 2004 .

[9]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.

[10]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[11]  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.

[12]  John A. Hertz,et al.  Modeling Genetic Regulatory Dynamics in Neural Development , 2002, J. Comput. Biol..

[13]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[14]  J. Barker,et al.  Large-scale temporal gene expression mapping of central nervous system development. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[15]  Patrik D'haeseleer,et al.  Linear Modeling of mRNA Expression Levels During CNS Development and Injury , 1998, Pacific Symposium on Biocomputing.

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

[17]  Gary D. Stormo,et al.  Modeling Regulatory Networks with Weight Matrices , 1998, Pacific Symposium on Biocomputing.

[18]  T. Hughes,et al.  Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. , 2000, Science.

[19]  E. Winzeler,et al.  Genomics, gene expression and DNA arrays , 2000, Nature.

[20]  Jesper Tegnér,et al.  Reverse engineering gene networks using singular value decomposition and robust regression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Satoru Miyano,et al.  Inferring Gene Regulatory Networks from Time-Ordered Gene Expression Data Using Differential Equations , 2002, Discovery Science.

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

[23]  V. Anne Smith,et al.  Evaluating functional network inference using simulations of complex biological systems , 2002, ISMB.

[24]  A. Schulze,et al.  Navigating gene expression using microarrays — a technology review , 2001, Nature Cell Biology.

[25]  M Wahde,et al.  Coarse-grained reverse engineering of genetic regulatory networks. , 2000, Bio Systems.

[26]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[27]  John Quackenbush.  Microarrays--Guilt by Association , 2003, Science.

[28]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.

[29]  Aurélien Mazurie,et al.  Gene networks inference using dynamic Bayesian networks , 2003, ECCB.

[30]  John Quackenbush.  Genomics. Microarrays--guilt by association. , 2003, Science.

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