Overview
- Based on the lectures by Prof. Wiehe, University of Cologne, Germany, and Prof. Haubold, University of Applied Sciences Weihenstephan, Freising, Germany
- Contains exercises, questions and answers to selected problems
- Includes supplementary material: sn.pub/extras
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (12 chapters)
-
Introduction
Keywords
About this book
Analysis of molecular sequence data is the main subject of this introduction to computational biology. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors.
This textbook is intended for students enrolled in courses in computational biology or bioinformatics as well as for molecular biologists, mathematicians, and computer scientists.
Reviews
Authors and Affiliations
Bibliographic Information
Book Title: Introduction to Computational Biology
Book Subtitle: An Evolutionary Approach
Authors: Bernhard Haubold, Thomas Wiehe
DOI: https://doi.org/10.1007/3-7643-7387-3
Publisher: Birkhäuser Basel
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Birkhäuser Basel 2006
Hardcover ISBN: 978-3-7643-6700-8Published: 18 May 2006
eBook ISBN: 978-3-7643-7387-0Published: 09 August 2006
Edition Number: 1
Number of Pages: XIV, 328
Topics: Life Sciences, general, Theory of Computation, Bioinformatics, Evolutionary Biology, Cell Biology, Genetics and Population Dynamics