Jointly sponsored by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), under the auspices of the DIMACS/BioMaPS/MB Center Special Focus on Information Processing in Biology, the Columbia University Center for the Multiscale Analysis of Genetic Networks (MAGNet), and the NIH Roadmap Initiative
This special focus is jointly sponsored by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), the Biological, Mathematical, and Physical Sciences Interfaces Institute for Quantitative Biology (BioMaPS), and the Rutgers Center for Molecular Biophysics and Biophysical Chemistry (MB Center).
Thursday, September 7, 2006 8:00 - 8:40 Breakfast, Registration and Poster Hanging 8:40 - 9:00 The DREAM project and the goals of this conference G. Stolovitzky and A. Califano (Opening remarks) 9:00 - 9:50 Keynote Presentation: Single Nucleotides in the P53 Pathway Arnold Levine, Institute for Advanced Study Session 1. Establishing Gold Standards for Reverse Engineering: Experimental Models 9:50 - 10:30 Inferring Regulatory Pathways: Data and experimental design Dana Pe'er (Invited Presentation) 10:30 - 11:10 Simulations and Multifactorial Gene Perturbation Experiments as a Way to Validate Reverse Engineered Gene Networks Reconstructed via the Integration of Genetic and Gene Expression Data Eric Schadt (Invited Presentation) 11:10 - 11:30 Coffee Break 11:30 - 11:50 Benchmarking reverse-engineering strategies via a synthetic gene network in Saccharomyces cerevisiae I. Cantone, D. di Bernardo, and M.P. Cosma 11:50 - 12:10 Dynamic pathway modeling: Feasibility analysis and optimal experimental design T. Maiwald, C. Kreutz, S. Bohl, A.C. Pfeifer, U. Klingmüller, and J. Timmer 12:10 - 12:30 The gap gene system of Drosophila melanogaster: Model-fitting and validation Theodore J. Perkins 12:30 - 2:00 Lunch and Poster Viewing Session 2. Establishing Gold Standards for Reverse Engineering: In-Silico Models 2:00 - 2:40 In Silico Models for Reverse Engineering - Complexity and Realism versus Well-Defined Metrics Pedro Mendes (Invited Presentation) 2:40 - 3:20 In Silico Gold Standards from Virtual Cell Leslie Loew (Invited Presentation) 3:20 - 3:40 Coffee Break 3:40 - 4:00 Reverse Engineering of Network Topology B. Stigler, M. Stillman, A. Jarrah, P. Mendes, and R. Laubenbacher 4:00 - 4:20 Data requirements of reverse-engineering algorithms Winfried Just 4:20 - 4:40 Reconstruction of metabolic networks from high throughput metabolic profiling data: in silico analysis of Red Blood Cell metabolism I. Nemenman, M.E. Wall, G.S. Escola, W.S. Hlavacek 4:40 - 6:00 Coffee and Poster Viewing Friday, September 8, 2006 8:00 - 8:40 Breakfast and Registration Session 3. Reverse Engineering: Data Generation and Inference Validation 9:00 - 9:40 R. Dalla-Favera (Invited Presentation) - TBA 9:40 - 10:10 Experimental Gold Standards for Reverse Engineering Network Connections J. Bader (Invited Presentation) 10:10 - 11:30 Coffee Break and Poster Viewing 11:30 - 11:50 Genome-scale mapping and global validation of the E. coli transcriptional network using a compendium of Affymetrix expression profiles B. Hayete, J.J. Faith, J.T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J. J. Collins, and T.S. Gardner 11:50 - 12:10 Using Data Fusions and Biomolecular Modeling towards Improving the Results of Reverse Engineering in Biological Networks. The ENRICHed Approach Michael Samoilov and Adam Arkin 12:10 - 12:30 Learning regulatory programs that accurately predict differential expression with MEDUSA A. Kundaje, D. Quigley, S. Lianoglou, X. Li, M. Arias, C. Wiggins, L. Zhang, and C. Leslie 12:30 Lunch 1:00 - 1:50 Nuclear Pore Complex: The hole picture? M.P. Rout (Keynote Presentation) 1:50 - 2:00 Break Session 4. Reverse Engineering Algorithms and Metrics for Inference Evalauation 2:00 - 2:40 Reverse Engineering Gene-Protein Networks J.J. Collins (Invited Presentation) 2:40 - 3:20 Understanding Biological Function through Evaluation of Genome-scale Networks M. Gerstein (Invited Presentation) 3:20 - 3:40 Coffee Break 3:40 - 4:00 Computational Modeling of Fetal Erythroblasts Predicts Negative NAutoregulatory Interactions Mediated by Fas and its ligand M. Socolovsky, M. Murrell, Y. Liu, R. Pop, E. Porpiglia, and A. Levchenko 4:00 - 4:20 Quantifying Reliability of Dynamic Bayesian Networks L. David and C. Wiggins 4:20 - 4:40 Evaluating Algorithms for Learning Biological Networks A. Bernard and A.J. Hartemink 4:40 - 5:40 Panel Discussion: The DREAM project 5:40 - 6:00 Summary and future perspectives for the DREAM project G. Stolovitzky and A. Califano (Closing Remarks) 6:00 Meeting AdjournedPosters
Inferring sequence specificity, condition-specific activity, and functional regulatory targets of yeast transcription factors by integrative modeling of Mrna expression data, ChIP-chip data, and genomic sequence B.C. Foat and H.J. Bussemaker Proteomic Network Consensus Modeling Over Multiple Discretizations E. Allen, J. Fetrow and D. John Proteomic Network Consensus Modeling Over Multiple Discretizations; part II: Robustness E. Allen, J. Fetrow and D. John Protein-Protein Interaction Prediction Enhanced by Incorporating Phylogenetic Tree Information R. Craig and L. Liao Benchmarking reverse engineering algorithms, in silico testing and meta-algorithms V. Belcastro, M. Bansal and D. di Bernardo Algorithmic Issue in Reverse Engineering of Protein and Gene Networks via Randomized Approximation Algorithms for Set Multicover Problems P. Berman, B. DasGupta and E. Sontag A framework for elucidating regulatory networks based on prior information and expression data O. Gevaert, S. Van Vooren and B. De Moor Network Legos: Building Blocks of Cellular Wiring Diagrams C.G. Rivera and T. M. Murali CellFrame: a data structure for cell biology and construction of cell perturbation networks Y. Gong and Z. Zhang Alternative pathway approach for automating analysis and validation of cell perturbation networks and design of perturbation experiments Y. Gong and Z. Zhang Inferring gene networks from microarray data by closed-loop optimization F. Emmert-Streib and D. Zhu Extracting falsifiable predictions from sloppy models R. Gutenkunst, F. Casey, J. Waterfall, C. Myers and J. Sethna Comparing reverse-engineering methods using an artificial biochemical network with transcription, translation and metabolism D. Camacho, P. Vera-Licona, R. Laubenbacher and P. Mendes Sensitivity Analysis for a mathematical model of the TNFa-mediated NF-kB-Ik signaling module Jaewook Joo, Steve Plimpton, Alexander Slepoy and Jean-Loup Faulon