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Computational Biology

  • Book
  • © 2010

Overview

Editors:
  1. David Fenyö

    1. The Rockefeller University, New York, USA

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  • Presents a broad survey of computational biology methods by focusing on their applications
  • Aids scientists in building the foundation to the eventual goal of modeling complex systems like an entire cell
  • Includes detailed descriptions of techniques provided by key experts from around the world
  • Includes supplementary material: sn.pub/extras

Part of the book series: Methods in Molecular Biology (MIMB, volume 673)

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Table of contents (19 protocols)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Sequencing and Genome Assembly Using Next-Generation Technologies

    • Niranjan Nagarajan, Mihai Pop
    Pages 1-17

  3. RNA Structure Prediction

    • István Miklós
    Pages 19-36

  4. Normalization of Gene-Expression Microarray Data

    • Stefano Calza, Yudi Pawitan
    Pages 37-52

  5. Prediction of Transmembrane Topology and Signal Peptide Given a Protein’s Amino Acid Sequence

    • Lukas Käll
    Pages 53-62

  6. Protein Structure Modeling

    • Lars Malmström, David R. Goodlett
    Pages 63-72

  7. Template-Based Protein Structure Modeling

    • Andras Fiser
    Pages 73-94

  8. Automated Protein NMR Structure Determination in Solution

    • Wolfram Gronwald, Hans Robert Kalbitzer
    Pages 95-127

  9. Computational Tools in Protein Crystallography

    • Deepti Jain, Valerie Lamour
    Pages 129-156

  10. 3-D Structures of Macromolecules Using Single-Particle Analysis in EMAN

    • Steven J. Ludtke
    Pages 157-173

  11. Computational Design of Chimeric Protein Libraries for Directed Evolution

    • Jonathan J. Silberg, Peter Q. Nguyen, Taylor Stevenson
    Pages 175-188

  12. Mass Spectrometric Protein Identification Using the Global Proteome Machine

    • David Fenyö, Jan Eriksson, Ronald Beavis
    Pages 189-202

  13. Unbiased Detection of Posttranslational Modifications Using Mass Spectrometry

    • Maria Fälth Savitski, Mikhail M. Savitski
    Pages 203-210

  14. Protein Quantitation Using Mass Spectrometry

    • Guoan Zhang, Beatrix M. Ueberheide, Sofia Waldemarson, Sunnie Myung, Kelly Molloy, Jan Eriksson et al.
    Pages 211-222

  15. Modeling Experimental Design for Proteomics

    • Jan Eriksson, David Fenyö
    Pages 223-230

  16. A Functional Proteomic Study of the Trypanosoma brucei Nuclear Pore Complex: An Informatic Strategy

    • Jeffrey A. DeGrasse, Damien Devos
    Pages 231-238

  17. Inference of Signal Transduction Networks from Double Causal Evidence

    • Réka Albert, Bhaskar DasGupta, Eduardo Sontag
    Pages 239-251

  18. Reverse Engineering Gene Regulatory Networks Related to Quorum Sensing in the Plant Pathogen Pectobacterium atrosepticum

    • Kuang Lin, Dirk Husmeier, Frank Dondelinger, Claus D. Mayer, Hui Liu, Leighton Prichard et al.
    Pages 253-281

  19. Parameter Inference and Model Selection in Signaling Pathway Models

    • Tina Toni, Michael P. H. Stumpf
    Pages 283-295

  20. Genetic Algorithms and Their Application to In Silico Evolution of Genetic Regulatory Networks

    • Johannes F. Knabe, Katja Wegner, Chrystopher L. Nehaniv, Maria J. Schilstra
    Pages 297-321
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Keywords

About this book

Computational biology is an interdisciplinary field that applies mathematical, statistical, and computer science methods to answer biological questions, and its importance has only increased with the introduction of high-throughput techniques such as automatic DNA sequencing, comprehensive expression analysis with microarrays, and proteome analysis with modern mass spectrometry. In Computational Biology, expert practitioners present a broad survey of computational biology methods by focusing on their applications, including primary sequence analysis, protein structure elucidation, transcriptomics and proteomics data analysis, and exploration of protein interaction networks. As a volume in 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. Authoritative and easy to use, Computational Biology is an ideal guide for all scientists interested in quantitative biology.

Editors and Affiliations

  • The Rockefeller University, New York, USA

    David Fenyö

Bibliographic Information

  • Book Title: Computational Biology

  • Editors: David Fenyö

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-60761-842-3

  • Publisher: Humana Totowa, NJ

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC 2010

  • Hardcover ISBN: 978-1-60761-841-6Published: 23 September 2010

  • Softcover ISBN: 978-1-4939-6122-1Published: 23 August 2016

  • eBook ISBN: 978-1-60761-842-3Published: 11 September 2010

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 327

  • Topics: Bioinformatics, Systems Biology, Computer Appl. in Life Sciences

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