Computational biology

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Title:

Series:

Springer protocols

Methods in molecular biology ; 673

Publication Information:

New York, NY : Humana Press, c2010

Physical Description:

xi, 327 p. : ill. ; 26 cm.

ISBN:

9781607618416

Subject Term:

Computational biology

Computational biology -- Methods

Added Author:

Fenyo, David

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010267183	QH324.2 C66 2010	Open Access Book	Book	Searching... Unknown

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.

Niranjan Nagarajan and Mihai PopIstván MiklósStefano Calza and Yudi PawitanLukas KällLars Malmström and David R. GoodlettAndras FiserWolfram Gronwald and Hans Robert KalbitzerDeepti Jain and Valerie LamourSteven J. LudtkeJonathan J. Silberg and Peter Q. Nguyen and Taylor StevensonDavid Fenyö and Jan Eriksson and Ronald BeavisMaria Fälth Savitski and Mikhail M. SavitskiGuoan Zhang and Beatrix M. Ueberheide and Sofia Waldemarson and Sunnie Myung and Kelly Molloy and Jan Eriksson and Brian T. Chait and Thomas A. Neubert and David FenyöJan Eriksson and David FenyöJeffrey A. DeGrasse and Damien DevosRéka Albert and Bhaskar DasGupa and Eduardo SontagKuang Lin and Dirk Husmeier and Frank Dondelinger and Claus D. Mayer and Hui Liu and Leighton Prichard and George P.C. Salmond and Ian K. Toth and Paul R.J. BirchTina Toni and Michael P. H. StumpfJohannes F. Knabe and Katja Wegner Chrystopher L. Nehaniv and Maria J. Schilstra

Preface	p. v
Contributors	p. ix
1 Sequencing and Genome Assembly Using Next-Generation Technologies	p. 1
2 RNA Structure Prediction	p. 19
3 Normalization of Gene-Expression Microarray Data	p. 37
4 Prediction of Transmembrane Topology and Signal Peptide Given a Protein's Amino Acid Sequence	p. 53
5 Protein Structure Modeling	p. 63
6 Template-Based Protein Structure Modeling	p. 73
7 Automated Protein NMR Structure Determination in Solution	p. 95
8 Computational Tools in Protein Crystallography	p. 129
9 3-D Structures of Macromolecules Using Single-Particle Analysis in EMAN	p. 157
10 Computational Design of Chimeric Protein Libraries for Directed Evolution	p. 175
11 Mass Spectrometric Protein Identification Using the Global Proteome Machine	p. 189
12 Unbiased Detection of Posttranslational Modifications Using Mass Spectrometry	p. 203
13 Protein Quantitation Using Mass Spectrometry	p. 211
14 Modeling Experimental Design for Proteomics	p. 223
15 A Functional Proteomic Study of the Trypanosoma brucei Nuclear Pore Complex: An Informatic Strategy	p. 231
16 Inference of Signal Transduction Networks from Double Causal Evidence	p. 239
17 Reverse Engineering Gene Regulatory Networks Related to Quorum Sensing in the Plant Pathogen Pectobacterium atrosepticum	p. 253
18 Parameter Inference and Model Selection in Signaling Pathway Models	p. 283
19 Genetic Algorithms and Their Application to In Silico Evolution of Genetic Regulatory Networks	p. 297
Index	p. 323

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