Computing for Biologists

Python Programming and Principles
Erscheinungsjahr 2014
ISBN: 978-1-107-64218-8
Verlag: Cambridge University Press

Buch, Englisch, 224 Seiten, Format (B × H): 187 mm x 246 mm, Gewicht: 500 g

Computing is revolutionizing the practice of biology. This book, which assumes no prior computing experience, provides students with the tools to write their own Python programs and to understand fundamental concepts in computational biology and bioinformatics. Each major part of the book begins with a compelling biological question, followed by the algorithmic ideas and programming tools necessary to explore it: the origins of pathogenicity are examined using gene finding, the evolutionary history of sex determination systems is studied using sequence alignment, and the origin of modern humans is addressed using phylogenetic methods. In addition to providing general programming skills, this book explores the design of efficient algorithms, simulation, NP-hardness, and the maximum likelihood method, among other key concepts and methods. Easy-to-read and designed to equip students with the skills to write programs for solving a range of biological problems, the book is accompanied by numerous programming exercises, available at www.cs.hmc.edu/CFB.

Autoren/Hrsg.

Libeskind-Hadas, Ran

Bush, Eliot

Fachgebiete

• Interdisziplinäres Wissenschaften Wissenschaften Interdisziplinär Naturwissenschaften, Technik, Medizin
• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

Preface; Meet python; Part I. Python Versus Pathogens: 1. Computing GC content; 2. Pathogenicity islands; 3. Open reading frames and genes; 4. Finding genes (at last!); Part II. Sequence Alignment and Sex Determination: 5. Recursion; 6. The use-it-or-lose-it principle; 7. Dictionaries, memoization, and speed; 8. Sequence alignments and the evolution of sex chromosomes; Part III. Phylogenetic Reconstruction and the Origin of Modern Humans: 9. Representing and working with trees; 10. Drawing trees; 11. The UPGMA algorithm; Part IV. Additional Topics: 12. RNA secondary structure prediction; 13. Gene regulatory networks and the maximum likelihood method; 14. Birds, bees, and genetic algorithms; Where to go from here; Index.

Über Autor(innen)

Libeskind-Hadas, Ran
Ran Libeskind-Hadas is the R. Michael Shanahan Professor of Computer Science at Harvey Mudd College, USA, working in the areas of algorithms and computational biology. He is a recipient of both the Iris and Howard Critchell Professorship and the Joseph B. Platt Professorship for teaching, as well as the Distinguished Alumni Educator Award from the University of Illinois, Urbana-Champaign Department of Computer Science.

Bush, Eliot
Eliot Bush is Associate Professor of Biology at Harvey Mudd College, USA. His main research interest is the study of evolution. Among other things he has modeled the evolution of metabolism, characterized DNA methylation patterns in insects, developed algorithms for studying substitution bias in DNA, and analyzed a 30-million-year-old primate fossil. His teaching interests focus on incorporating computers and programming assignments into biology coursework.