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Computational Biology By David Fenyoe

Computational Biology by David Fenyoe

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Computational Biology Summary

Computational Biology by David Fenyoe

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 (TM) 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.

Table of Contents

1. Sequencing and Genome Assembly Using Next-Generation Technologies Niranjan Nagarajan and Mihai Pop 2. RNA Structure Prediction Istvan Miklos 3. Normalization of Gene-Expression Microarray Data Stefano Calza and Yudi Pawitan 4. Prediction of Transmembrane Topology and Signal Peptide Given a Proteins Amino Acid Sequence Lukas Kall 5. Protein Structure Modeling Lars Malmstroem and David R. Goodlett 6. Template-Based Protein Structure Modeling Andras Fiser 7. Automated Protein NMR Structure Determination in Solution Wolfram Gronwald and Hans Robert Kalbitzer 8. Computational Tools in Protein Crystallography Deepti Jain and Valerie Lamour 9. 3-D Structures of Macromolecules Using Single Particle Analysis in EMAN Steven J. Ludtke 10. Computational Design of Chimeric Protein Libraries for Directed Evolution Jonathan J. Silberg, Peter Q. Nguyen, and Taylor Stevenson 11. Mass Spectrometric Protein Identification Using the Global Proteome Machine David Fenyoe, Jan Eriksson, and Ronald Beavis 12. Unbiased Detection of Post Translational Modifications Using Mass Spectrometry Maria Falth Savitski and Mikhail M. Savitski 13. Protein Quantitation Using Mass Spectrometry Guoan Zhang, Beatrix M. Ueberheide, Sofia Waldemarson, Sunnie Myung, Kelly Molloy, Jan Eriksson, Brian T. Chait, Thomas A. Neubert, and David Fenyoe 14. Modeling Experimental Design for Proteomics Jan Eriksson and David Fenyoe 15. A Functional Proteomic Study of the Trypanosoma brucei Nuclear Pore Complex: An Informatic Strategy Jeffrey A. DeGrasse and Damien Devos 16. Inference of Signal Transduction Networks from Double Causal Evidence Reka Albert, Bhaskar DasGupta, and Eduardo Sontag 17. 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 Pritchard, George P.C. Salmond, Ian K. Toth, and Paul R.J. Birch 18. ParameterInference and Model Selection in Signaling Pathway Models Tina Toni and Michael P.H. Stumpf 19. Genetic Algorithms and Their Application to In silico Evolution of Genetic Regulatory Networks Johannes F. Knabe, Katja Wegner, Chrystopher L. Nehaniv, and Maria J. Schilstra

Additional information

Computational Biology by David Fenyoe
Humana Press Inc.
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