Computational Systems Biology

1. Front Matter

   Pages i-xviii

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2. Network Components

   1. Front Matter

      Pages 1-1

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   2. Identification of cis-Regulatory Elements in Gene Co-expression Networks Using A-GLAM

      • Leonardo Mariño-Ramírez, Kannan Tharakaraman, Olivier Bodenreider, John Spouge, David Landsman

      Pages 3-22

   3. Structure-Based Ab Initio Prediction of Transcription Factor–Binding Sites

      • L. Angela Liu, Joel S. Bader

      Pages 23-41

   4. Inferring Protein–Protein Interactions from Multiple Protein Domain Combinations

      • Simon P. Kanaan, Chengbang Huang, Stefan Wuchty, Danny Z. Chen, Jesús A. Izaguirre

      Pages 43-59

   5. Prediction of Protein–Protein Interactions: A Study of the Co-evolution Model

      • Itai Sharon, Jason V. Davis, Golan Yona

      Pages 61-88

   6. Computational Reconstruction of Protein–Protein Interaction Networks: Algorithms and Issues

      • Eric Franzosa, Bolan Linghu, Yu Xia

      Pages 89-100

   7. Prediction and Integration of Regulatory and Protein–Protein Interactions

      • Duangdao Wichadakul, Jason McDermott, Ram Samudrala

      Pages 101-143

   8. Detecting Hierarchical Modularity in Biological Networks

      • Erzsébet Ravasz

      Pages 145-160

3. Network Inference

   1. Front Matter

      Pages 1-1

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   2. Methods to Reconstruct and Compare Transcriptional Regulatory Networks

      • M. Madan Babu, Benjamin Lang, L. Aravind

      Pages 163-180

   3. Learning Global Models of Transcriptional Regulatory Networks from Data

      • Aviv Madar, Richard Bonneau

      Pages 181-210

   4. Inferring Molecular Interactions Pathways from eQTL Data

      • Imran Rashid, Jason McDermott, Ram Samudrala

      Pages 211-223

   5. Methods for the Inference of Biological Pathways and Networks

      • Roger E. Bumgarner, Ka Yee Yeung

      Pages 225-245

4. Network Dynamics

   1. Front Matter

      Pages 1-1

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   2. Exploring Pathways from Gene Co-expression to Network Dynamics

      • Huai Li, Yu Sun, Ming Zhan

      Pages 249-267

   3. Network Dynamics

      • Herbert M. Sauro

      Pages 269-309

   4. Kinetic Modeling of Biological Systems

      • Haluk Resat, Linda Petzold, Michel F. Pettigrew

      Pages 311-335

   5. Guidance for Data Collection and Computational Modelling of Regulatory Networks

      • Adam Christopher Palmer, Keith Edward Shearwin

      Pages 337-354

5. Function and Evolutionary Systems Biology

   1. Front Matter

      Pages 1-1

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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 andwider scale.

• Analysis
• algorithms
• bioinformatics
• evolution
• genes
• genome
• modeling
• molecular biology
• Systems Biology

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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