Computational Biology

1. Front Matter

   Pages i-xi

   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

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.

• 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
• Systems Biology

• The Rockefeller University, New York, USA

  David Fenyö

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