Computer Programming in Quantitative Biology covers the general background of Fortran coding and the more sophisticated computer programs likely to be encountered in quantitative biology. It discusses the application of over 40 appropriate and easily adaptable programming techniques to problems of major biological interest. Organized into 15 chapters, the book starts by providing an introductory outline of computer structure and function needed to appreciate many basic programming procedures. A chapter discusses some general principles underlying Fortran coding and the use of digital computers, with emphasis on major features of Fortran IV. Other chapters present short introduction to the statistical or mathematical techniques in each of the main sections under which program are described. These chapters also provide some aspects of matrix algebra that are essential for serious statistical programming and offer a general guide to efficiency in programming. All complete programs are accompanied by a flowchart and a detailed discussion. This book is a valuable source of information for biologists, computational biologists, research biologists, undergraduate students, and advanced or specialized students of biology.

A Primer for Computational Biology aims to provide life scientists and students the skills necessary for research in a data-rich world. The text covers accessing and using remote servers via the command-line, writing programs and pipelines for data analysis, and provides useful vocabulary for interdisciplinary work. The book is broken into three parts: Introduction to Unix/Linux: The command-line is the "natural environment" of scientific computing, and this part covers a wide range of topics, including logging in, working with files and directories, installing programs and writing scripts, and the powerful "pipe" operator for file and data manipulation. Programming in Python: Python is both a premier language for learning and a common choice in scientific software development. This part covers the basic concepts in programming (data types, if-statements and loops, functions) via examples of DNA-sequence analysis. This part also covers more complex subjects in software development such as objects and classes, modules, and APIs. Programming in R: The R language specializes in statistical data analysis, and is also quite useful for visualizing large datasets. This third part covers the basics of R as a programming language (data types, if-statements, functions, loops and when to use them) as well as techniques for large-scale, multi-test analyses. Other topics include S3 classes and data visualization with ggplot2.

Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language.

This textbook is for biologists, to conduct quantitative analysis and modeling of biological processes at molecular and cellular levels. Focusing on practical concepts and techniques for everyday research, this text will enable beginners, both students and established biologists, to take the first step in quantitative biology. It also provides step-by-step tutorials to run various sample programs in one's personal computer using Excel and Python. This volume traces topics, starting with an introductory chapter, such as modeling, construction and execution of numerical models, and key concepts in quantitative biology: feedback regulations, fluctuations and randomness, and statistical analyses. It also provide sample codes with guidance to procedure programming for actual biological processes such as movement of the nucleus within a cell, cell-cycle regulation, stripe pattern formation of skins, and distribution of energy. Written by a leading research scientist who has a background in biology, studied quantitative approaches by himself, and teaches quantitative biology at several universities, this textbook broadens quantitative approaches for biologists who do not have a strong background in mathematics, physics, or computer programming, and helps them progress further in their research.

Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational biology problems.Drawing on the author's first-hand exper

The area of biologically inspired computing, or biological computation, involves the development of new, biologically based techniques for solving difficult computational problems. A unified overview of computer science ideas inspired by biology, Biological Computation presents the most fundamental and significant concepts in this area. In the book

This book introduces Python as a powerful tool for the investigation of problems in computational biology, for novices and experienced programmers alike.