DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM)

September 7 - 8, 2006
Wave Hill Conference Center, New York, NY

Gustavo Stolovitzky, IBM Computational Biology Center, gustavo@us.ibm.com
Andrea Califano, Columbia University, califano@dbmi.columbia.edu
Jim Collins, Boston University, jcollins@bu.edu

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).

Workshop Program:

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 Adjourned
  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

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Document last modified on September 5, 2006.