Computational Systems Biology / Edition 1

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Computational systems biology is the term that we use to describe computational methods to identify, infer, model, and store relationships between the molecules, pathways, and cells (‘‘systems’’) involved in a living organism. Based on this definition, the field of computational systems biology has been in existence for some time. However, the recent confluence of high-throughput methodology for biological data gathering,genome-scalesequencing,andcomputationalprocessingpowerhasdrivena reinvention and expansion of this field. The expansions include not only modeling of small metabolic (1–3) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (5–15). Generally, these models provide a general overview of one or more aspects of these systems and leave the determination of details to experimentalists focused on smaller subsystems. The promise of such approaches is that they will elucidate patterns, relationships, and general features, which are not evident from examining specific components or subsystems. These predictions are either interesting in and of themselves (e. g. , the identification of an evolutionary pattern) or interesting andvaluabletoresearchersworkingonaparticularproblem(e. g. ,highlightapreviously unknown functional pathway). Two events have occurred to bring the field of computational systems biology to theforefront. Oneistheadventofhigh-throughputmethodsthathavegeneratedlarge amounts of information about particular systems in the form of genetic studies, gene and protein expression analyses and metabolomics. With such tools, research to c- sidersystemsasawholearebeingconceived,planned,andimplementedexperimentally on an ever more frequent and wider scale.

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Editorial Reviews

From the Publisher
From the reviews:

“The new volume of the Humana Press ‘Methods in Molecular Biology’ series, entitled ‘Computational Systems Biology,’ consists of 25 chapters authored by 57 specialists in the field. … This book contains a wide collection of methods that … gives a broad review of this fascinating and quickly developing field. … for a computational biologist, it is a fascinating read, a broad and comprehensive resource on the current methods and approaches.” (Borys Wróbel, Acta Biochimica Polonica, Vol. 56, December, 2009)

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Product Details

  • ISBN-13: 9781588299055
  • Publisher: Springer-Verlag New York, LLC
  • Publication date: 4/1/2009
  • Series: Methods in Molecular Biology Series, #541
  • Edition description: 2009
  • Edition number: 1
  • Pages: 587
  • Product dimensions: 7.40 (w) x 10.20 (h) x 1.30 (d)

Table of Contents

Part I. Network Components
1. Identification of cis-Regulatory Elements in Gene Co-Expression Networks Using A-GLAM
Leonardo Mariño-Ramirez, Kannan Tharakaraman, Oliver Bodenreider, John Spouge, and David Landsman
2. Structure-Based ab initio Prediction of Transcription Factor Binding Sites
L. Angela Liu and Joel S. Bader
3. Inferring Protein-Protein Interactions from Multiple Protein Domain Combinations
Simon P. Kanaan, Chengbang Huang, Stefan Wuchty, Danny Z. Chen, and Jesus Izaguirre
4. Prediction of Protein-Protein Interactions: A Study of the Co-Evolution Model
Itai Sharon, Jason V. Davis, and Golan Yona
5. Computational Reconstruction of Protein-Protein Interaction Networks: Algorithms and Issues
Eric Franzosa, Bolan Linghu, and Yu Xia
6. Prediction and Integration of Regulatory and Protein-Protein Interactions
Duangdao Wichadakul, Jason McDermott, and Ram Samudrala
7. Detecting Hierarchical Modulary in Biological Networks
Erzsébet Ravasz Regan

Part II. Network Inference
8. Methods to Reconstruct and Compare Transcriptional Regulatory Networks
M. Madan Babu, Benjamin Lang, and L. Aravind
9. Learning Global Models of Transcriptional Regulatory Networks from Data
Aviv Madar and Richard Bonneau
10. Inferring Molecular Interactions Pathways from eQTL Data
Imran Rashid, Jason McDermott, and Ram Samudrala
11. Methods for the Inference of Biological Pathways and Networks
Roger E. Bumgarner and Ka Yee Yeung

Part III. Network Dynamics
12. Exploring Pathways from Gene Co-Expression to Network Dynamics
Huai Li, Yu Sun, and Ming Zhan
13. Network Dynamics
Herbert M. Sauro
14. Kinetic Modeling of Biological Systems
Haluk Resat, Linda Petzold, and Michel F. Pettigrew
15. Guidance for DataCollection and Computational Modeling of Regulatory Networks
Adam Christopher Palmer and Keith Edward Shearwin

Part IV. Function and Evolutionary Systems Biology
16. A Maximum Likelihood Method for Reconstruction of the Evolution of Eukaryotic Gene Structure
Liran Carmel, Igor B. Rogozin, Yuri I. Wolf, and Eugene V. Koonin
17. Enzyme Function Prediction with Interpretable Models
Umar Syed and Golan Yona
18. Using Evolutionary Information to Find Specificity Determining and Co-Evolving Residues
Grigory Kolesov and Leonid A. Mirny
19. Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics
Jake Y. Chen, Eunseog Youn, and Sean D. Mooney
20. Effects of Functional Bias on Supervised Learning of a Gene Network Model
Insuk Lee and Edward M. Marcotte

Part V. Computational Infrastructure for Systems Biology
21. Comparing Algorithms for Clustering of Expression Data: How to Assess Gene Clusters
Golan Yona, William Dirks, and Shafquat Rahman
22. The Bioverse API and Web Application
Michal Guerquin, Jason McDermott, Zach Frazier, and Ram Samudrala
23. Computational Representation of Biological Systems
Zach Frazier, Jason McDermott, Michal Guerquin, and Ram Samudrala
24. Biological Network Inference and Analysis using SEBINI and CABIN
Ronald Taylor and Mudita Singhal

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