Computational Systems Biology

1. Function and Evolutionary Systems Biology

   1. A Maximum Likelihood Method for Reconstruction of the Evolution of Eukaryotic Gene Structure

      • Liran Carmel, Igor B. Rogozin, Yuri I. Wolf, Eugene V. Koonin

      Pages 357-371

   2. Enzyme Function Prediction with Interpretable Models

      • Umar Syed, Golan Yona

      Pages 373-420

   3. Using Evolutionary Information to Find Specificity-Determining and Co-evolving Residues

      • Grigory Kolesov, Leonid A. Mirny

      Pages 421-448

   4. Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics

      • Jake Y. Chen, Eunseog Youn, Sean D. Mooney

      Pages 449-461

   5. Effects of Functional Bias on Supervised Learning of a Gene Network Model

      • Insuk Lee, Edward M. Marcotte

      Pages 463-475

2. Computational Infrastructure for Systems Biology

   1. Front Matter

      Pages 1-1

      PDF

   2. Comparing Algorithms for Clustering of Expression Data: How to Assess Gene Clusters

      • Golan Yona, William Dirks, Shafquat Rahman

      Pages 479-509

   3. The Bioverse API and Web Application

      • Michal Guerquin, Jason McDermott, Zach Frazier, Ram Samudrala

      Pages 511-534

   4. Computational Representation of Biological Systems

      • Zach Frazier, Jason McDermott, Michal Guerquin, Ram Samudrala

      Pages 535-549

   5. Biological Network Inference and Analysis Using SEBINI and CABIN

      • Ronald Taylor, Mudita Singhal

      Pages 551-576

3. Back Matter

   Pages 577-592

   PDF

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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