Network Inference

• Predicting Protein Complex Membership Using Probabilistic Network Reliability
  S. Asthana, O.D, King, F.D. Gibbons and F.P. Roth
  [PDF] Genome Research , May 2004.

• Computational discovery of gene modules and regulatory networks
  Z. Bar-Joseph, G.K. Gerber, T.I. Lee, N.J. Rinaldi, J.Y. Yoo, F. Robert, D.B. Gordon, E. Fraenkel, T.S. Jaakkola, R.A. Young and D.K. Gifford
  [PDF] Nature Biotechnology , August 2004

• Validation of qualitative models of genetic regulator networks by model checking: analysis of the nutritional stress response in Escherichia coli
  G. Batt, D. Ropers, H. de Jong, J. Geiselmann, R. Mateescu, M. Page and D. Schneider
  [PDF] Bioinformatics , 20045

• A Bayesian approach to reconstructing genetic regulatory networks with hidden factors
  M.J. Beal, F. Falciani, Z. Ghahramani, C. Rangel and D.L. Wild
  [PDF] Bioinformatics , 2004

• Iterative signature algorithm for the analysis of large-scale gene expression data
  S. Bergmann, J. Ihmels and N. Barkai
  [PDF] Physical Review E , March 2003

• A duplication growth model of gene expression networks
  A. Bhan, D.J. Galas and T.G. Dewey
  [PDF] Bioinformatics , 2002

• Discovering the Hidden Structure of Complex Dynamic Systems
  X. Boyen, N. Friedman and D. Koller
  [PDF] Uncertainty in Artificial Intelligence (UAI 1999) , 1999

• Inferring gene networks from steady-state response to single-gene perturbations
  P. Brazhnik
  [PDF] J. Theoretical Biology , 2005

• A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae
  K. Chen, T. Wang, H. Tseng, C.F. Huang and C. Kao
  [PDF] Bioinformatics , 2005

• Modeling Gene Expression With Differential Equations
  T. Chen, H.L. He and G.M. Church
  [PDF] Pacific Symposium on Biocomputing , 1999

• Modeling and Simulation of Genetic Regulatory Systems: A Literature Review
  H. De Jong
  [PDF] J. Computational Biology , 2002

• EXAMINE: A computational approach to reconstructing gene regulatory networks
  X. Deng, H. Geng and H. Ali
  [PDF] BioSystems , 2005

• Genetic network inference: from co-expression clustering to reverse engineering
  P. D'haeseleer, S. Liang and R. Somogyi
  [PDF] Bioinformatics , 2000

• Inferring Cellular Networks Using Probabilistic Graphical Models
  N. Friedman
  [PDF] Science , 6 February 2004

• Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
  T.S. Gardner, D. di Bernardo. D. Lorenz and J.J. Collins
  [PDF] Science , 4 July 2003

• Dynamic network reconstruction from gene expression data applied to immune response during bacterial infection
  R. Guthke, U. Moller, M. Hoffmann, F. Thies and S. Topfer
  [PDF] Bioinformatics , 2004

• Combining Location And Expression Data For Principled Discovery of Genetic Regulatory Network Models
  A.J. Hartemink, D.K. Gifford, T.S. Jaakkola and R.A. Young
  [PDF] Pacific Symposium on Biocomputing , 2002

• Using Graphical Models And Genomic Expression Data To Statistically Validate Models of Genetic Regulatory Networks
  A.J. Hartemink, D.K. Gifford, T.S. Jaakkola and R.A. Young
  [PDF] Pacific Symposium on Biocomputing , 2002

• A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data
  R. Jansen, H. Yu, D. Greenbaum, Y. Kluger, N.J. Krogan, S. Chung, A. Emili, M. Snyder, J.F. Greenblatt and M. Gerstein
  [PDF] Science , October 2003

• Regulatory motif finding by logic regression
  S. Keles, M.J. van der Laan and C. Vulpe
  [PDF] Bioinformatics , 2004

• A Computational Algebra Approach to the Reverse Engineering of Gene Regulatory Networks
  R. Laubenbacher and B. Stigler
  [PDF] arxiv preprint , 2004

• A Probabilistic Functional Network of Yeast Genes
  I. Lee, S.V. Date, A.T. Adai and E.M. Marcotte
  [PDF] Science , November 2004
  paper in relatively recent PLOS - analysis of evolution of cis-regulatory DNA motifs in a functional module. This shows beautifully an example of conserved FUNCTION with underlying variation and evolution of the DNA sequences in one of the ways Tony mentioned (but not in all ways he suggested). Here SOME elements of the network structure and outputs are remarkably retained also, but other parts are not. I think it will be important for us to pick this apart to look at exactly how it would likely fare if we were to approach it without knowing the answers and how we hope our probabilistic model of network structure would treat it - and how network function model would treat it,

• Gene Network Models Applied to Questions in Development and Evolution
  G. Marnellos
  [PostScript] Ph.D thesis Yale University, May 1997

• A Gene Network Approach to Modeling Early Neurogenesis in Drosophila
  G. Marnellos and E. Mjolsness
  [PDF] Pacific Symposium on Biocomputing, January 1998

• Inferring quantitative models of regulatory networks from expression data
  I. Nachman, A. Regev and N. Friedman
  [PDF] Bioinformatics , 2004

• Information theory, multivariate dependence, and genetic network inference
  I. Nemenman
  [PDF] arxiv preprint , 2004

• Superiority of network motifs over optimal networks and an application to the revelation of gene network evolution
  S. Ott, A. Hansen, S.-Y Kim and S. Miyano
  [PDF] Bioinformatics , 2005

• Splitting the dynamics of large biochemical interaction networks
  E. Pecou
  [PDF] J. Theoretical Biology , 2005

• Inferring models of gene expression dynamics
  T.J. Perkins. M. Hallett and L. Glass
  [PDF] J. Theoretical Biology , 7 October 2004

• Reconstruction of gene networks using Bayesian learning and manipulation experiments
  I. Poumara and L. Wernisch
  [PDF] Bioinformatics , 2004

• Modeling T-cell activation using gene expression profiling and state-space models
  C. Rangel, J. Angus, Z. Ghahramani, M. Lioumi. E. Sotheran, A. Gaiba, D.L. Wild and F. Falciani
  [PDF] Bioinformatics , 2004

• Model for cooperative control of positional information in Drosophila by bicoid and maternal hunchback
  J. Reinitz, E. Mjolsness, D.H. Sharp
  J. Exp Zool. , January 1995

• A Bayesian regression approach to the inference of regulatory networks from gene expression data
  S. Rogers and M. Girolami
  [PDF] Bioinformatics , 2005

• Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data
  E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, D. Koller and N. Friedman
  [PDF] Nature Genetics , June 2003

• Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data
  E. Sontag, A. Kiyatkin and B.N. Kholodenko
  [PDF] Bioinformatics , 2004

• Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data
  A. Tanay, R. Sharan, M. Kupiec and R. Shamir
  [PDF] PNAS , March 2004

• Systematic determination of genetic network architecture
  S. Tavazoie, J.D. Hughes, M.J. Campbell, R.J. Cho and G.M. Church
  [PDF] Nature Genetics , July 1999

• Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling
  J. Tegner, M.K.S. Yeung, J. Hasty and J.J. Collins
  [PDF] PNAS , May 13, 2003

• A model-based optimization framework for the inference of gene regulatory networks from DNA array data
  R. Thomas, S. Mehrotra, E.T. Papoutsakis and V. Hatzimanikatis
  [PDF] Bioinformatics , 2004

• Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling
  H. Toh and K. Horimoto
  [PDF] Bioinformatics , 2002

• Modeling Regulatory Networks With Weight Matrices
  D.C. Weaver, C.T. Workman and G.D. Stormo
  [PDF] Pacific Symposium on Biocomputing , 1999

• A Comparison Of Genetic Network Models
  L.F.A. Wessels, E. P. Van Someren and M.J.T. Reinders
  [PDF] Pacific Symposium on Biocomputing , 2001

• Linear Modeling of Genetic Networks from Experimental Data
  E.P. van Someren, L.F.A. Wessels and M.J.T. Reinders
  [PDF] Pacific Symposium on Biocomputing , 2002

• Reverse engineering gene networks using singular value decomposition and robust regression
  M.K.S. Yeung, J. Tegner and J.J. Collins
  [PDF] PNAS , April 30, 2002

• A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks
  X. Zhou, X. Wang, R. Pal, I. Ivanov, M. Bittner and E.R. Dougherty
  [PDF] Bioinformatics , 2004

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Network Evolution

• A duplication growth model of gene expression networks
  A. Bhan, D.J. Galas and T.G. Dewey
  [PDF] Bioinformatics , 2002

• Duplication Models for Biological Networks
  F. Chung, L. Lu, T.G. Dewey and D.J. Galas
  [PDF] J. of Computational Biology , 2003

• Functional Evolution of a cis-Regulatory module
  M.Z. Ludwig, A. Palsson, E. Alekseeva, C.M. Bergman, J. Nathan and M. Kreitman
  [PDF] PLoS Biology , April 2005
  Marcotte and coworkers combining multiple kinds of large and smaller scale data to derive connectivity map with probability measures. example in yeast uses RNA expression data, citation data, protein Interaction data etc. lacks protein:DNA in vivo interaction data

• Superiority of network motifs over optimal networks and an application to the revelation of gene network evolution
  S. Ott, A. Hansen, S.-Y Kim and S. Miyano
  [PDF] Bioinformatics , 2005

• Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome
  A. Rzhetzky and S.M. Gomez
  [PDF] Bioinformatics , 2001

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Biological Networks

• On schemes of combinatorial transcription logic
  N.E. Buchler, U. Gerland and T. Hwa
  [PDF] PNAS , April 29, 2003

• Integrative Analysis of Cell Cycle Control in Budding Yeast
  K.C. Chen, L. Calzone, A. Csikasz-Nagy, F.R. Cross, B. Novak and J.J. Tyson
  [PDF] Mol. Biol. Cell , August 2004

• A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae
  K. Chen, T. Wang, H. Tseng, C.F. Huang and C. Kao
  [PDF] Bioinformatics , 2005

• A plethora of sites
  G. Euskirchen and M. Snyder
  [PDF] Nature Genetics , April 2004

• The yeast cell-cycle network is robustly designed
  F. Li, T. Long, Y. Lu, Q. Ouyang and C. Tang
  [PDF] PNAS , April 2004

• A global view of epistasis
  J.H. Moore
  [PDF] Nature Genetics , January 2005

• Model for cooperative control of positional information in Drosophila by bicoid and maternal hunchback
  J. Reinitz, E. Mjolsness, D.H. Sharp
  J. Exp Zool. , January 1995

• Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data
  E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, D. Koller and N. Friedman
  [PDF] Nature Genetics , June 2003

• Modular epistasis in yeast metabolism
  D. Segre, A. DeLuna, G.M. Church and R. Kishony
  [PDF] Nature Genetics , January 2005

• Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data
  A. Tanay, R. Sharan, M. Kupiec and R. Shamir
  [PDF] PNAS , March 2004

• Genome-Wide Prediction of C. elegans Genetic Interactions
  W. Zhong and P. W. Sternberg
  [PDF] Science , March 2006

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Network Analysis

• Alignement of metabolic pathways
  R.Y. Pinter, O. Rokhlenko, E. Yeger-Lotem and M. Ziv-Ukelson
  [PDF] Bioinformatics , 2005

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Plant Models

• A Mathematical Model of Auxin-mediated Radial Growth in Trees
  E.M Kramer
  [PDF] J. theor. Biol, 2001

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Data Analysis

• A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inference of gene changes
  P. Baldi and A.D. Long
  [PDF] Bioinformatics , 2001

• Deconvolving cell cycle expression data with complementary information
  Z. Bar-Joseph, S. Farkash, D.K. Gifford, I. Simon and R. Rosenfeld
  [PDF] Bioinformatics , August 2004

• Knowledge-based analysis of microarray gene expression data by using support vector machines
  M.P.S. Brown, W.N.B. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Furey, M. Ares and D. Haussler
  [PDF] PNAS , January 4, 2000

• Analysis Techniques for Microarray Time-Series Data
  V. Filkov, S. Skiena and J. Zhi
  [PDF] J. Computational Biology , 2002

• Biologically valid linear factor models of gene expression
  M. Girolami and R. Breitling
  [PDF] Bioinformatics , 2004

• Machine Learning for Science: State of the Art and Future Prospects
  E. Mjolsness and D. DeCoste
  [PDF] Science , 14 September 2001

• Stochastic chemical kinetics and the quasi-steady-state assumption: Application to the Gillespie algorithm
  C.V. Rao and A. P. Arkin
  [PDF] J. Chemical Physics, March 2003

• Using hidden Markov models to analyze gene expression time course data
  A. Schliep, A. Schonhuth and C. Steinhoff
  [PDF] Bioinformatics , 2003

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General Biology

• A plethora of sites
  G. Euskirchen and M. Snyder
  [PDF] Nature Genetics , April 2004

• The RNAi revolution
  C.D. Novina and P.A. Sharp
  [PDF] Nature , July 2004