Research and Markets: The Challenges of Systems Biology - Community Efforts to Harness Biological Complexity

DUBLIN--(Business Wire)--
Research and Markets
(http://www.researchandmarkets.com/research/787c3e/the_challenges_of) has
announced the addition of John Wiley and Sons Ltd's new report "The Challenges
of Systems Biology: Community Efforts to Harness Biological Complexity" to their
offering.

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.

Key Topics Covered:

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

* Ranking genes by their co-expression to subsets of pathway members: Priit
Adler, Hedi Peterson, Phaedra Agius, Jüri Reimand, and Jaak Vilo
* 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
* 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
* 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
* Specification of spatial relationships in directed graphs of cell signaling
networks: Azi Lipshtat, Susana R. Neves, and Ravi Iyengar
* Uncovering metabolic objectives pursued by changes of enzyme levels:
* Sabrina Hoffmann and Hermann-Georg Holzhütter. Biological Network Models
* Modeling of Gene Regulatory Network Dynamics Using Threshold Logic: Tejaswi
Gowda, Sarma Vrudhula, and Seungchan Kim
* Global robustness and identifiability of random, scale-free and small-world
networks: Yunchen Gong and Zhaolei Zhang Reverse Engineering Algorithms
* DREAM Project: The five-gene-network data analysis with Local Causal Discovery
Algorithm using Causal Bayesian networks: Changwon Yoo and Erik Brilz
* Combining multiple results of a network reverse engineering algorithm: Daniel
Marbach, Claudio Mattiussi, and Dario Floreano
* Network inference by combining biologically motivated regulatory constraints
with penalized regression: Fabio Parisi, Heinz Koeppl, and Felix
Naef.Computation Tools for Reverse Engineering
* A Gene Network Simulator to Assess Reverse Engineering Algorithms: Barbara Di
Camillo, Gianna Toffolo, and Claudio Cobelli
* 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
* 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
* 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
* 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
* 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
* 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
* Replaying the Evolutionary Tape: Biomimetic Reverse Engineering of Gene
Networks: Daniel Marbach, Claudio Mattiussi, and Dario Floreano
* 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
* NIRest: a tool for gene network and mode of action inference: Mario Lauria,
Francesco Iorioa, and Diego di Bernardo
* Reverse Engineering of Gene Networks with LASSO and Non-Linear Basis
Functions: Mika Gustafsson, Michael Hörnquist, Jesper Lundström, Johan
Björkegren, and Jesper Tegnér
* Prediction of Pair-wise Gene Interaction Using Threshold Logic: Tejaswi Gowda,
Sarma Vrudhul, and Seungchan Kim
* 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
* 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

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Research and Markets
Laura Wood, Senior Manager
press@researchandmarkets.com
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