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Computational Systems Biology

Handbook / Manual

Series: Methods in Molecular Biology Volume: 541

By: Jason McDermott

600 pages, Illus

Humana Press

Hardback | Jun 2009 | #179429 | ISBN-13: 9781588299055
Availability: Usually dispatched within 1 week Details
NHBS Price: £123.00 $162/€140 approx

About this book

The recent confluence of high throughput methodology for biological data gathering, genome-scale sequencing, and computational processing power has driven a reinvention and expansion of the way we identify, infer, model, and store relationships between molecules, pathways, and cells in living organisms. In "Computational Systems Biology", expert investigators contribute chapters which bring together biological data and computational and/or mathematical models of the data to aid researchers striving to create a system that provides both predictive and mechanistic information for a model organism.

The volume is organized into five major sections involving network components, network inference, network dynamics, function and evolutionary system biology, and computational infrastructure for systems biology. As a volume of the highly successful "Methods in Molecular Biology" series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results. Comprehensive and up-to-date, "Computational Systems Biology" serves to motivate and inspire all those who wish to develop a complete description of a biological system.

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 Wrobel, Acta Biochimica Polonica, Vol. 56, December, 2009)


Part I. Network Components1. Identification of cis-Regulatory Elements in Gene Co-Expression Networks Using A-GLAMLeonardo Marino-Ramirez, Kannan Tharakaraman, Oliver Bodenreider, John Spouge, and David Landsman2. Structure-Based ab initio Prediction of Transcription Factor Binding SitesL. Angela Liu and Joel S. Bader3. Inferring Protein-Protein Interactions from Multiple Protein Domain CombinationsSimon P. Kanaan, Chengbang Huang, Stefan Wuchty, Danny Z. Chen, and Jesus Izaguirre4. Prediction of Protein-Protein Interactions: A Study of the Co-Evolution ModelItai Sharon, Jason V. Davis, and Golan Yona5. Computational Reconstruction of Protein-Protein Interaction Networks: Algorithms and IssuesEric Franzosa, Bolan Linghu, and Yu Xia6. Prediction and Integration of Regulatory and Protein-Protein InteractionsDuangdao Wichadakul, Jason McDermott, and Ram Samudrala7. Detecting Hierarchical Modulary in Biological NetworksErzsebet Ravasz ReganPart II. Network Inference8. Methods to Reconstruct and Compare Transcriptional Regulatory NetworksM. Madan Babu, Benjamin Lang, and L. Aravind9. Learning Global Models of Transcriptional Regulatory Networks from DataAviv Madar and Richard Bonneau10. Inferring Molecular Interactions Pathways from eQTL DataImran Rashid, Jason McDermott, and Ram Samudrala11. Methods for the Inference of Biological Pathways and NetworksRoger E. Bumgarner and Ka Yee YeungPart III. Network Dynamics12. Exploring Pathways from Gene Co-Expression to Network DynamicsHuai Li, Yu Sun, and Ming Zhan13. Network DynamicsHerbert M. Sauro14. Kinetic Modeling of Biological SystemsHaluk Resat, Linda Petzold, and Michel F. Pettigrew15. Guidance for Data Collection and Computational Modeling of Regulatory NetworksAdam Christopher Palmer and Keith Edward ShearwinPart IV. Function and Evolutionary Systems Biology16. A Maximum Likelihood Method for Reconstruction of the Evolution of Eukaryotic Gene StructureLiran Carmel, Igor B. Rogozin, Yuri I. Wolf, and Eugene V. Koonin17. Enzyme Function Prediction with Interpretable ModelsUmar Syed and Golan Yona18. Using Evolutionary Information to Find Specificity Determining and Co-Evolving ResiduesGrigory Kolesov and Leonid A. Mirny19. Connecting Protein Interaction Data, Mutations, and Disease Using Bioinformatics Jake Y. Chen, Eunseog Youn, and Sean D. Mooney20. Effects of Functional Bias on Supervised Learning of a Gene Network ModelInsuk Lee and Edward M. MarcottePart V. Computational Infrastructure for Systems Biology21. Comparing Algorithms for Clustering of Expression Data: How to Assess Gene ClustersGolan Yona, William Dirks, and Shafquat Rahman22. The Bioverse API and Web ApplicationMichal Guerquin, Jason McDermott, Zach Frazier, and Ram Samudrala23. Computational Representation of Biological SystemsZach Frazier, Jason McDermott, Michal Guerquin, and Ram Samudrala24. Biological Network Inference and Analysis using SEBINI and CABINRonald Taylor and Mudita Singhal

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