Computing for Biologists: Python Programming And Principles

Computing for Biologists: Python Programming And Principles
  • eBook:
    Computing for Biologists: Python Programming And Principles
  • Author:
    Ran Libeskind-Hadas
  • Edition:
    1 edition
  • Categories:
  • Data:
    September 22, 2014
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    218 pages
  • Format:

Book Description
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 (dot) edu/CFB.


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 Alignment and the Evolution of Sex Chromosomes

Part III - Phlyogenetic 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

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