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Computational Systems Biology

  • Kartonierter Einband
  • 612 Seiten
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This volume sees expert investigators contribute chapters which bring together biological data and computational/mathematical models of the data to aid researchers create a system that provides both predictive and mechanistic information for a model organism.

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 (13) and signaling systems (2, 4) but also modeling of the relati- ships between biological components in very large systems, including whole cells and organisms (515). 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.

Presents a broad range of topics related to computational systems biology, including mechanistic modeling, high-throughput data analysis, biological network analysis and data representation and management

Provides a number of clearly described, detailed 'recipes' for analysis of systems data

Supplies a number of alternative approaches to modeling similar types of data

Includes supplementary material: sn.pub/extras


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.

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


Titel: Computational Systems Biology
EAN: 9781493956449
ISBN: 1493956442
Format: Kartonierter Einband
Herausgeber: Humana Press
Anzahl Seiten: 612
Gewicht: 1258g
Größe: H260mm x B193mm x T32mm
Jahr: 2016
Untertitel: Englisch
Auflage: Softcover reprint of the original 1st ed. 2009

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