Computational Biology

340 Pages·2010·6.321 MB·English

by  Niranjan Nagarajan, Mihai Pop (auth.), David Fenyö (eds.)

M M B ™
ethods in olecular iology
Series Editor
John M. Walker
School of Life Sciences
University of Hertfordshire
Hatfield, Hertfordshire, AL10 9AB, UK
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Computational Biology
Edited by
David Fenyö
The Rockefeller University, New York, NY, USA
Editor
David Fenyö
The Rockefeller University
1230 York Avenue
New York, NY 10065
USA
[email protected]
ISSN 1064-3745 e -ISSN 1940-6029
ISBN 978-1-60761-841-6 e-ISBN 978-1-60761-842-3
DOI 10.1007/978-1-60761-842-3
Springer New York Dordrecht Heidelberg London
Library of Congress Control Number: 2010934634
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Cover Illustration Caption: Different experimental observation deposited in GPMDB of peptides from PSF2 a protein
associated with the replication fork. Observed peptides are shown in red and regions of the protein that are difficult
to observe in proteomics experiments are shown in green. In a majority of the 20 experiments shown, the same
5 proteotypic peptides are observed.
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Preface
Computational biology is an interdisciplinary field that applies mathematical, statistical,
and computer science methods to answer biological questions. The importance of compu-
tational biology has 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. Computational methods are not only
critical for the effective analysis of the large amount of data from these experiments but
also important to fully utilize this wealth of information to provide more realistic models
of biological systems. The ultimate goal of modeling complex systems like an entire cell
might be far in the future, but computational methods are essential in building the foun-
dation that will allow this goal to be reached. The primary purpose of this book is to pres-
ent 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.
New York, NY David Fenyö
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Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
1 Sequencing and Genome Assembly Using Next-Generation Technologies....... 1
Niranjan Nagarajan and Mihai Pop
2 RNA Structure Prediction........................................... 19
István Miklós
3 Normalization of Gene-Expression Microarray Data....................... 37
Stefano Calza and Yudi Pawitan
4 Prediction of Transmembrane Topology and Signal Peptide
Given a Protein’s Amino Acid Sequence ................................ 53
Lukas Käll
5 Protein Structure Modeling ......................................... 63
Lars Malmström and David R. Goodlett
6 Template-Based Protein Structure Modeling............................. 73
Andras Fiser
7 Automated Protein NMR Structure Determination in Solution............... 95
Wolfram Gronwald and Hans Robert Kalbitzer
8 Computational Tools in Protein Crystallography.......................... 129
Deepti Jain and Valerie Lamour
9 3-D Structures of Macromolecules Using Single-Particle Analysis in EMAN..... 157
Steven J. Ludtke
10 Computational Design of Chimeric Protein Libraries
for Directed Evolution ............................................. 175
Jonathan J. Silberg, Peter Q. Nguyen, and Taylor Stevenson
11 Mass Spectrometric Protein Identification Using the Global
Proteome Machine................................................ 189
David Fenyö, Jan Eriksson, and Ronald Beavis
12 Unbiased Detection of Posttranslational Modifications Using
Mass Spectrometry................................................ 203
Maria Fälth Savitski and Mikhail M. Savitski
13 Protein Quantitation Using Mass Spectrometry .......................... 211
Guoan Zhang, Beatrix M. Ueberheide, Sofia Waldemarson,
Sunnie Myung, Kelly Molloy, Jan Eriksson, Brian T. Chait,
Thomas A. Neubert, and David Fenyö
14 Modeling Experimental Design for Proteomics........................... 223
Jan Eriksson and David Fenyö
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viii Contents
15 A Functional Proteomic Study of the Trypanosoma brucei Nuclear
Pore Complex: An Informatic Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Jeffrey A. DeGrasse and Damien Devos
16 Inference of Signal Transduction Networks from Double Causal Evidence ...... 239
Réka Albert, Bhaskar DasGupta, and Eduardo Sontag
17 Reverse Engineering Gene Regulatory Networks Related to Quorum Sensing
in the Plant Pathogen Pectobacterium atrosepticum ........................ 253
Kuang Lin, Dirk Husmeier, Frank Dondelinger, Claus D. Mayer, Hui Liu,
Leighton Prichard, George P.C. Salmond, Ian K. Toth, and Paul R.J. Birch
18 Parameter Inference and Model Selection in Signaling Pathway Models ........ 283
Tina Toni and Michael P. H. Stumpf
19 Genetic Algorithms and Their Application to In Silico Evolution
of Genetic Regulatory Networks...................................... 297
Johannes F. Knabe, Katja Wegner, Chrystopher L. Nehaniv,
and Maria J. Schilstra
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
Contributors
Réka albeRt • Department of Physics, Penn State University,
University Park, PA, USA
Ronald beavis • The Biomedical Research Centre, University of British Columbia,
Vancouver, BC, Canada
Paul R. J. biRch • Scottish Crop Research Institute, Dundee, UK
stefano calza • Department of Medical Epidemiology and Biostatistics,
Karolinska Institute, Stockholm, Sweden
bRian t. chait • The Rockefeller University, New York, NY, USA
bhaskaR dasGuPta • Department of Computer Science, University of Illinois
at Chicago, Chicago, IL, USA
JeffRey a. deGRasse • U .S . Food and Drug Administration, College Park, MD, USA
damien devos • Structural Bioinformatics, European Molecular Biology Laboratory,
Heidelberg, Germany
fRank dondelinGeR • Biomathematics & Statistics Scotland and School
of Informatics, University of Edinburgh, Edinburgh, UK
Jan eRiksson • Department of Chemistry, Swedish University of Agricultural Sciences,
Uppsala, Sweden
maRia fälth savitski • Unit Cancer Genome Research, Division of Molecular
Genetics, Heidelberg, Germany
david fenyö • The Rockefeller University, 1230 York Avenue, New York,
NY 10065, USA
andRas fiseR • Department of Systems and Computational Biology & Department
for Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
david R. Goodlett • Department of Medicinal Chemistry,
University of Washington, Seattle, Washington, USA
WolfRam GRonWald • Institute for Biophysics and Physical Biochemistry,
University of Regensburg, Regensburg, Germany
diRk husmeieR • Biomathematics and Statistics Scotland, Edinburgh & Aberdeen, UK
deePti Jain • National Centre for Biological Sciences, Bangalore, India
hans RobeRt kalbitzeR • Institute for Biophysics and Physical Biochemistry,
University of Regensburg, Regensburg, Germany
lukas käll • Department of Biochemistry and Biophysics,
Center for Biomembrane Research and Stockholm Bioinformatics Center,
Stockholm University, Stockholm, Sweden
Johannes f. knabe • Biological and Neural Computation Laboratory
and Adaptive Systems Research Group, STRI, University of Hertfordshire,
Hatfield, Hertfordshire, UK
valeRie lamouR • Institute of Genetics and Molecular and Cellular Biology,
Illkirch, France
kuanG lin • Biomathematics and Statistics Scotland, Edinburgh & Aberdeen, UK
hui liu • Scottish Crop Research Institute, Dundee, UK
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