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Computational Biology

  • David Fenyö

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

Table of contents

  1. Front Matter
    Pages i-xi
  2. István Miklós
    Pages 19-36
  3. Stefano Calza, Yudi Pawitan
    Pages 37-52
  4. Lars Malmström, David R. Goodlett
    Pages 63-72
  5. Wolfram Gronwald, Hans Robert Kalbitzer
    Pages 95-127
  6. Deepti Jain, Valerie Lamour
    Pages 129-156
  7. Jonathan J. Silberg, Peter Q. Nguyen, Taylor Stevenson
    Pages 175-188
  8. David Fenyö, Jan Eriksson, Ronald Beavis
    Pages 189-202
  9. Maria Fälth Savitski, Mikhail M. Savitski
    Pages 203-210
  10. Guoan Zhang, Beatrix M. Ueberheide, Sofia Waldemarson, Sunnie Myung, Kelly Molloy, Jan Eriksson et al.
    Pages 211-222
  11. Jan Eriksson, David Fenyö
    Pages 223-230
  12. Réka Albert, Bhaskar DasGupta, Eduardo Sontag
    Pages 239-251
  13. Kuang Lin, Dirk Husmeier, Frank Dondelinger, Claus D. Mayer, Hui Liu, Leighton Prichard et al.
    Pages 253-281
  14. Tina Toni, Michael P. H. Stumpf
    Pages 283-295
  15. Johannes F. Knabe, Katja Wegner, Chrystopher L. Nehaniv, Maria J. Schilstra
    Pages 297-321
  16. Back Matter
    Pages 323-327

About this book

Introduction

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.

Keywords

Computer science methods DNA DNA sequencing High-throughput techniques In silico Mass spectrometry Mathematics Microarray Microarrays Protein structure elucidation Statistics Translation protein structure sequence analysis signal transduction

Editors and affiliations

  • David Fenyö
    • 1
  1. 1.The Rockefeller UniversityNew YorkUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-60761-842-3
  • Copyright Information Springer Science+Business Media, LLC 2010
  • Publisher Name Humana Press, Totowa, NJ
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-60761-841-6
  • Online ISBN 978-1-60761-842-3
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • Buy this book on publisher's site
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