Reverse Engineering Biological Networks

Computational biologists are striving to "reverse engineer" the underlying networks of interactions between the molecules in the cell. This volume and the conference it reports on attempt a systematic evaluation of reverse engineering methods. The DREAM project brings together a diverse group of researchers to clarify potentials and limitations of the enterprise of reverse engineering cellular networks. An important aspiration of the project is to compare the effectiveness of different methods in reverse engineering biological networks. Evaluating this requires a "gold standard" network for which at least the true topology of connections is known. Many participants, especially the computational biologists, believe that synthetic networks are good candidates for this purpose because, at least for now, only they can be described with certainty. Experimental biologists, however, worry that unless the project addresses real biological networks, it could evolve into a mathematical exercise with little impact on biology. These and other ideas are discussed.NOTE: Annals volumes are available for sale as individual books or as a journal. For information on institutional journal subscriptions, please visit www.blackwellpublishing.com/nyas.ACADEMY MEMBERS: Please contact the New York Academy of Sciences directly to place your order (www.nyas.org). Members of the New York Academy of Science receive full-text access to the Annals online and discounts on print volumes. Please visit www.nyas.org/membership/main.asp for more information about becoming a member.

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.

Preface
Community Efforts for Pathway Inference
Dialogue on Reverse Engineering Assessment and Methods: the DREAM of High Throughput Pathway Inference
ENFIN - A Network to Enhance Integrative Systems Biology
Overview of Reverse Engineering Methods: Experiment and Theory
Reconstructing Signal Transduction Pathways: Challenges and Opportunities
Theory and Limitations of Genetic Network Inference from Microarray Data
Establishing In-Silico and Experimental Gold Standards and Performance Metrics for Reverse Engineering
Comparison of Reverse Engineering Methods Using an In-Silico Network
Benchmarking of Dynamic Bayesian Networks From Stochastic Time-Series Data
Reconstruction of Metabolic Networks from High-throughput Metabolite Profiling Data: In-Silico Analysis of Red Blood Cell Metabolism
The Gap Gene System of Drosophila Melanogaster: Model-fitting and Validation
Theoretical Analyses of Reverse Engineering Algorithms
Algorithmic Issues in Reverse Engineering of Protein and Gene Networks via the Modular Response Analysis Method
Data Requirements of Reverse-engineering Algorithms
Some Reverse Engineering Algorithms
Improving Protein-Protein Interaction Prediction based on Phylogenetic Information using Least-Squares SVM
Reverse-Engineering of Dynamic Networks
Learning Regulatory Programs that Accurately Predict Differential Expression with MEDUSA
Reverse Engineering of Parameters in Quantitative Models
Extracting Falsifiable Predictions from Sloppy Models
Dynamic Pathway Modeling: Feasibility Analysis and Optimal Experimental Design
Sensitivity Analysis of Computational Model of the IKK-NF-?B-I?B?-A20 Signal Transduction Network
Integration of Prior Information in Reverse Engineering Algorithms
A Framework for Elucidating Regulatory Networks Based on Prior Information and Expression Data
CellFrame: A Data Structure for Abstraction of Cell Biology Experiments and Construction of Perturbation Networks
Alternative Pathway Approach for Automating Analysis and Validation of Cell Perturbation Networks and Design of Perturbation Experiments
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Reverse Engineering Biological Networks Opportunities and Challenges in Computational Methods for Pathway Inference, Volume 1118

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