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The Challenges of Systems Biology. Community Efforts to Harness Biological Complexity, Volume 1158. Edition No. 1. Annals of the New York Academy of Sciences

  • ID: 2183655
  • Book
  • June 2009
  • Region: United States
  • 300 Pages
  • John Wiley and Sons Ltd
At a microscopic level, organisms are ruled by interacting systems of biomolecules. Historically, scientists painstakingly elucidated chains of molecular events using experiments that reveal individual interactions, although they recognized that members of different pathways frequently interact. In recent years, researchers have built richer, interconnected networks to mathematically summarize their knowledge of these interactions. This systems biology enterprise, largely stimulated by high-throughput tools like microarrays that measure mRNA levels as an indicator of gene expression, is a vital and increasingly important activity in both basic biology and in medicine.

A nagging concern, however, is how accurately these networks represent the biology. For complex systems like biological networks, there are practical limits on how well even massive amounts of data can uniquely define the underlying structure and yield useful predictions of measurable events. Indeed, although its advocates call this process "reverse engineering," the topology and the detailed molecular interactions of the "inferred" networks will likely never be known with precision.

This volume captures the ongoing process to assess the ability of scientists - and their computer servants - to infer networks from experimental data, by comparing their predictions to "gold-standard" networks whose structure is thought to be known.

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Preface: Gustavo Stolovitzky, Pascal Kahlem, and Andrea Califano

Part I: Selected Papers from the ENFIN-DREAM Joint Conference

1. Ranking genes by their co-expression to subsets of pathway members: Priit Adler, Hedi Peterson, Phaedra Agius, Jüri Reimand, and Jaak Vilo

2. Creating reference datasets for Systems Biology applications using text mining: Martin Krallinger, Ana Maria Rojas, and Alfonso Valencia Selected Papers from the DREAM2 Conference Transcriptional Modules and Regulatory Networks

3. Condition-dependent combinatorial regulation in Escherichia coli: Karen Lemmens, Tijl De Bie, Thomas Dhollander, Pieter Monsieurs, Bart De Moor, Julio Collado-Vides, Kristof Engelen, and Kathleen Marchal

4. Reverse-engineering transcriptional modules from gene expression data: Tom Michoel, Riet De Smet , Anagha Joshi, Kathleen Marchal and Yves Van de Peer Signaling and Metabolic Networks

5. Specification of spatial relationships in directed graphs of cell signaling networks:  Azi Lipshtat, Susana R. Neves, and Ravi Iyengar

6. Uncovering metabolic objectives pursued by changes of enzyme levels:

Sabrina Hoffmann and Hermann-Georg Holzhütter. Biological Network Models

7. Modeling of Gene Regulatory Network Dynamics Using Threshold Logic: Tejaswi Gowda, Sarma Vrudhula, and Seungchan Kim

8. Global robustness and identifiability of random, scale-free and small-world networks: Yunchen Gong and Zhaolei Zhang Reverse Engineering Algorithms

9. DREAM Project: The five-gene-network data analysis with Local Causal Discovery Algorithm using Causal Bayesian networks: Changwon Yoo and Erik Brilz

10. Combining multiple results of a network reverse engineering algorithm: Daniel Marbach, Claudio Mattiussi, and Dario Floreano

11. Network inference by combining biologically motivated regulatory constraints with penalized regression: Fabio Parisi, Heinz Koeppl, and Felix Naef.Computation Tools for Reverse Engineering

12. A Gene Network Simulator to Assess Reverse Engineering Algorithms: Barbara Di Camillo, Gianna Toffolo, and Claudio Cobelli

13. A Network Inference Workflow Applied to Virulence-related Processes in Salmonella typhimurium: Ronald C. Taylor, Mudita Singhal, Jennifer Weller, Saeed Khoshnevis, Liang Shi, and Jason McDermott

Part II: Best Performer Papers from the DREAM2 Challenges

Overview of the DREAM2 Challenges

14. Lessons from the DREAM2 Challenges: a community effort to assess biological network inference: Gustavo Stolovitzky, Robert J. Prill, and Andrea Califano The BCL6 Target Discovery Challenge Best Performer Papers

15. DREAM2 Challenge: Integrated Multi-Array Supervised Learning Algorithm for BCL-6 Transcriptional Targets Prediction:  W.H. Lee, V. Narang, H. Xu, F. Lin, K.C. Chin, W.K. Sung

16. A data integration framework for prediction of transcription factor targets: a BCL6 case study: Matti Nykter, Harri Lähdesmäki, Alistair Rust, Vesteinn Thorsson, and Ilya Shmulevich

17. Inferring direct regulatory targets of a transcription factor in the DREAM2 Challenge: Vinsensius B. Vega, Xing Yi Woo, Habib Hamidi, Hock Chuan Yeo, Zhen Xuan Yeo, Guillaume Bourque, and Neil D. Clarke The Protein–Protein Interaction Challenge Best Performer Paper

18. A Probabilistic Graph-theoretic Approach to Integrate Multiple Predictions for the Protein-Protein Subnetwork Prediction Challenge: Chua Hon Nian, Hugo Willy, Liu Guimei, Li Xiaoli, Wong Limsoon, Ng See-Kiong The Five Gene Network Challenges Best Performer Papers

19. Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene Networks: Daniel Marbach, Claudio Mattiussi, and Dario Floreano

20. Inferring Gene Networks: Dream or nightmare? Part 1: Challenges 1 and 3: Angela Baralla, Wieslawa Mentzen, Alberto de la Fuente

The in Silico Network Challenges Best Performer Papers

21. NIRest: a tool for gene network and mode of action inference: Mario Lauria, Francesco Iorioa, and Diego di Bernardo

22. Reverse Engineering of Gene Networks with LASSO and Non-Linear Basis Functions: Mika Gustafsson, Michael Hörnquist, Jesper Lundström, Johan Björkegren, and Jesper Tegnér

23. Prediction of Pair-wise Gene Interaction Using Threshold Logic: Tejaswi Gowda, Sarma Vrudhul, and Seungchan Kim

24. Inferring Gene Networks: Dream or nightmare? Part 2: Challenges 4 and 5: Alan Scheinine, Wieslawa Mentzen, E. Pieroni, F. Maggio, G. Mancosu, and Alberto de la Fuente The Genome Scale Challenge Best Performer Paper

25.  Inference of regulatory gene interactions from expression data using three-way mutual information: John Watkinson, Kuo-ching Liang, Xiaodong Wang, Tian Zheng and Dimitris Anastassiou

Index of Contributors

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Gustavo Stolovitzky IBM Computational Biology Center.

Pascal Kahlem EMBL – European Bioinformatics Institute.

Andrea Califano Columbia University.
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