Reverse Engineering Biological Networks

Gustavo Stolovitzky is the editor of Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference, Volume 1118, published by Wiley. Andrea Califano is the editor of Reverse Engineering Biological Networks: Opportunities and Challenges in Computational Methods for Pathway Inference, Volume 1118, published by Wiley.

"The volume particularly excels at its primary goal, which is to honestly describe the progress made in network construction and the challenges still facing researchers." (The Quarterly Review of Biology, June 2010)

• Preface: Gustavo Stolovitzky. Part I: Community Efforts for Pathway Inference:.1. Dialogue on Reverse Engineering Assessment and Methods: the DREAM of High Throughput Pathway Inference: Gustavo Stolovitzky, Don Monroe, Andrea Califano.2. ENFIN - A Network to Enhance Integrative Systems Biology: Pascal Kahlem and Ewan Birney.Part II: Overview of Reverse Engineering Methods: Experiment and Theory:.3. Reconstructing Signal Transduction Pathways: Challenges and Opportunities: Arnold J. Levine, Wenwei Hu, Zhaohui Feng and German Gil.4. Theory and Limitations of Genetic Network Inference from Microarray Data: Adam A. Margolin and Andrea Califano.Part III: Establishing In-Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering:.5. Comparison of Reverse Engineering Methods Using an In-Silico Network: Diogo Camacho, Paola Vera Licona, Pedro Mendes and Reinhard Laubenbacher.6. Benchmarking of Dynamic Bayesian Networks From Stochastic Time-Series Data: Lawrence A. David and Chris H. Wiggins.7. Reconstruction of Metabolic Networks from High-throughput Metabolite Profiling Data: In-Silico Analysis of Red Blood Cell Metabolism: Ilya Nemenman, G. Sean Escola, William S. Hlavacek, Pat J. Unkefer,Clifford J. Unkefer and Michael E. Wall.8. The Gap Gene System of Drosophila Melanogaster: Model-fitting and Validation: Theodore J. Perkins.Part IV: Theoretical Analyses of Reverse Engineering Algorithms:.9. Algorithmic Issues in Reverse Engineering of Protein and Gene Networks via the Modular Response Analysis Method: Piotr Berman, Bhaskar DasGupta, and Eduardo Sontag.10. Data Requirements of Reverse-engineering Algorithms: Winfried Just.Part V: Some Reverse Engineering Algorithms:.11. Improving Protein-Protein Interaction Prediction based on Phylogenetic Information using Least-Squares SVM: Roger A. Craig and Li Liao.12. Reverse-Engineering of Dynamic Networks: Brandy Stigler, Abdul Jarrah, Mike Stillman and Reinhard Laubenbacher.13. Learning Regulatory Programs that Accurately Predict Differential Expression with MEDUSA: Anshul Kundaje, Steve Lianoglou, Xuejing Li, David Quigle, Marta Arias, Chris H. Wiggins, Li Zhang and Christina Leslie.Part VI: Reverse Engineering of Parameters in Quantitative Models:.14. Extracting Falsifiable Predictions from Sloppy Models: Ryan N. Gutenkunst, Fergal P. Casey, Joshua J. Waterfall, Christopher R. Myers and James P. Sethna.15. Dynamic Pathway Modeling: Feasibility Analysis and Optimal Experimental Design: Thomas Maiwald, Clemens Kreutz, Andrea C. Pfeifer, Sebastian Bohl, Ursula Klingmüller and Jens Timmer.16. Sensitivity Analysis of Computational Model of the IKK-NF-ĸB-IĸBά-A20 Signal Transduction Network: Jaewook Joo, Steve Plimpton, Shawn Martin, Laura Swiler and Jean-Loup Faulon.Part VII: Integration of Prior Information in Reverse Engineering Algorithms:.17. A Framework for Elucidating Regulatory Networks Based on Prior Information and Expression Data: Olivier Gevaert, Steven Van Vooren and Bart De Moor.18. CellFrame: A Data Structure for Abstraction of Cell Biology Experiments and Construction of Perturbation Networks: Yunchen Gong and Zhaolei Zhang.19. Alternative Pathway Approach for Automating Analysis and Validation of Cell Perturbation Networks and Design of Perturbation Experiments: Yunchen Gong and Zhaolei Zhang