Reverse Engineering Biological Networks

Opportunities and Challenges in Computational Methods for Pathway Inference, Volume 1118
1. Auflage 2007
ISBN: 978-1-57331-689-7
Verlag: Wiley

Buch, Englisch, 452 Seiten, Format (B × H): 155 mm x 229 mm, Gewicht: 490 g

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.

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Autoren/Hrsg.

Stolovitzky, Gustavo

Califano, Andrea

Inhaltsverzeichnis

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 Dy

Über Autor(innen)

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.