Python For The Life Sciences
Python For The Life Sciences (Python For the Life Sciences (1st Edition))
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Python For The Life Sciences

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Completed on 2017-02-07

About the Book

“Fun, entertaining, witty and darn useful. A magical portal to the big data revolution”
-- Sandro Santagata, Assistant Professor in Pathology, Harvard Medical School
“Alex and Gordon’s enthusiasm for Python is contagious"
-- Glenys Thomson, Professor of Integrative Biology, University of California, Berkeley
"A lovely book with humor and perspective"
-- John Novembre, Associate Professor of Human Genetics, University of Chicago
"Python for the Life Sciences is an excellent tutorial"
-- Steven J. Mack, Assistant Scientist, Children's Hospital Oakland Research Institute

If you’re a biologist with little or no experience in programming, but who would like to learn how to write code, Python For The Life Sciences is probably the book you’ve been waiting for!

Python For The Life Sciences is an intuitive and easy-to-follow introduction to computer programming, written specifically for biologists with no computer programming experience.

Virtually all of the examples in the book are drawn from across a wide spectrum of life science research, from simple biochemical calculations and sequence analysis, to modeling the dynamic interactions of genes and proteins in cells, or the drift of genes in an evolving population.

Best of all, we show you how to implement all of these projects in Python, one of the most popular programming languages for scientific computing - a language so versatile and easy to use, that we call it The Swiss Army Knife of Programming Languages.  

To get the academic discount for students and teachers, email us at info@amberbiology.com using your email account for your accredited school or college, tell us who are and what department you’re in, and we’ll send you a coupon to purchase the book at the current academic price ($30.00).

The authors of this book are both computational biologists and principals at the digital biology consulting firm Amber Biology, based in Cambridge Massachusetts.

For updates about the book and special offers, please sign up to our mailing list.

About the Authors

Gordon Webster
Gordon Webster

After completing a PhD in biophysics and structural biology at the University of London, Gordon has worked in life science R&D in both Europe and the U.S., with a particular emphasis on molecular engineering and computational biology. In academic and commercial environments ranging from universities and medical schools to small venture capital-funded startups and global pharmaceutical companies, he has served in a diversity of roles from research faculty to company vice president.

Gordon is the author of numerous original scientific articles and patents and has created and managed some very successful research partnerships with industrial, academic and government organizations. He initiated and managed the first translational oncology clinical trial at a multinational pharmaceutical company and has coached and led research project teams in large matrix organizations, as well as large, distributed teams of scientists. software developers and technical specialists, working together across multiple time zones. Gordon’s career path has always reflected his belief that the most interesting and potentially promising areas of research lie at the intersections between the traditional scientific disciplines.

Alex Lancaster
Alex Lancaster

Alex Lancaster is an evolutionary biologist, engineer, writer and consultant. Alex completed his Ph.D. in evolutionary biology at the University of California, Berkeley, and also holds bachelor's degrees in physics and electrical engineering. He has worked in research & development in both Australia and the United States with a major focus on evolutionary and systems biology. He has also worked extensively in genomics, analyzing next-generation sequencing data and has developed tools for clinical and population genomics, with a particular specialization in immunogenetic applications. He has held research and faculty positions in academia, as well as R&D positions in the broadcasting and IT industries.

Alex has published many peer-reviewed papers and is interested in solving problems in biology using evolutionary and complex adaptive systems approaches. He has done pioneering work in this area as a co-developer of the open-source agent-based modeling toolkit, Swarm, one of the first tools for large-scale modeling of collective behavior in biology and beyond. He is passionate about the power of open source and open science approaches to accelerate discovery.

Website: Biosystems Analytics

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Python For the Life Sciences (1st Edition)

This is the very first release of the full Python For The Life Sciences book.

  • English

  • PDF

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  • MOBI

$19.95
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Python For the Life Sciences (1st Edition) - Pack of 5 copies

This is the very first release of the full Python For The Life Sciences book (5 copies).

  • English

  • PDF

  • EPUB

  • MOBI

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Python For the Life Sciences (1st Edition) - Pack of 10 copies

This is the very first release of the full Python For The Life Sciences book (10 copies).

  • English

  • PDF

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  • MOBI

$172.00
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Reader Testimonials

Sandro Santagata
Sandro Santagata

Assistant Professor in Pathology, Harvard Medical School

Fun, entertaining, witty and darn useful. A magical portal to the big data revolution

Glenys Thomson
Glenys Thomson

Professor of Integrative Biology, University of California, Berkeley

Alex and Gordon’s enthusiasm for Python is contagious

John Novembre
John Novembre

Associate Professor of Human Genetics, University of Chicago

A lovely book with humor and perspective

Table of Contents

Prologue: Welcome to the kingdom of Nerdia

Getting started with Python: Setting yourself up to use Python

We get you started on your journey, all the fun details on getting your very own Python environment setup up.

Python at the lab bench: The fundamentals of the Python language

We introduce some Python fundamentals and show you how to ditch those calculators and spreadsheets and let Python relieve the drudgery of basic lab calculations (freeing up more valuable time to drink coffee and play Minecraft)

Making sense of sequences: Biological sequences and Python data structures

We introduce basic Python string and character handling and demonstrate Python’s innate awesomeness for handling nucleic acid and protein sequences.

A statistical interlude: Of Bayes theorem and biomarkers

Here we discuss Bayes’ Theorem and implement it in Python, illustrating in the process why even your doctor might not always estimate your risk of cancer correctly.

Open doors to your data: Reading, parsing and handling biological data files

Did we already mention how great Python is for handling biological sequence data? In this chapter we expand our discussion to sequence file formats like FASTA.

Finding needles in haystacks: Regular expressions for genomic and sequences 

In this chapter we show how to search even the largest of biological sequences quickly and efficiently using Python Regular Expressions – and in the process, blow the lid off the myth that Python has to be slow because it is an interpreted language.

Object lessons: Biological sequences as Python objects

Just when you thought you had heard the last about sequences, we explore the foundational concept of Object Oriented Programming in Python, and demonstrate a more advanced and robust approach to handling biological sequences using Python objects.

Slicing and dicing genomic data: Next generation sequencing pipelines

We demonstrate how easy it is to use Python to create a simple next-generation sequencing pipeline – and how it can be used to extract data from many kinds of genomic sources, up to and including whole genomes.

The wells! The wells!: Microtiter plate assays I: data structures

We use Python to manage data from that trusty workhorse of biological assays, the 96-well plate.

Well on the way: Microtiter plate assays II: automation and visualization

Building on the previous chapter, we add lab automation and handy-dandy visualizations using Python's matplotlib library.

Molecules in 3D: Mathematics and linear algebra for structural biology

In which we demonstrate Python’s ability to implement three-dimensional mathematics and linear algebra for molecular mechanics. It’s nano but it’s still biology folks!

Turning genes on and off: Visualizing biochemical kinetics using matplotlib

In which we use Python to recreate what happens in the biochemist’s beaker (minus the nasty smells) – as well as using Python to model the cooperative binding effects of allosteric proteins.

Taming the network hairball: Using Python sets to mine systems biology data

In which we demonstrate how to parse and interrogate network data using Python sets, and in the process, tame the complex network “hairball”.

Genetic feedback loops: Modeling gene networks with the Gillespie algorithm 

In which we introduce the Gillespie algorithm to model biological noise and switches in cells, and use Python to implement it and visualize the results – along with some pretty pictures to delight the eye.

Growing a virtual garden: Modeling plant development with L-systems

In which we introduce Lindemeyer systems to grow virtual plants and use Python’s implementation of Turtle LOGO. Don’t worry, these plants will not invade your garden (but they might take over your computer).

How the leopard got its spots: Cellular automata models of Turing patterns 

In which we use the power of cellular automata to grow some dandy leopard skin pants using Turing’s model of morphogenesis with Python 2D graphics. Note to our readers: no leopards were harmed in the writing of this chapter.

Foxes guarding henhouses: Ecological modeling with predator-prey dynamics

In which we let loose chickens and foxes into an ecosystem and let ‘em duke it out in a state-space that is visualized using Python’s animation features.

A virtual flu epidemic: Exploring epidemiology with agent-based models

In which we create a virtual flu epidemic with agents that have internal state and behaviors. This illustrates an approach in which Python’s object-oriented programming approach really shines.

Retracing life’s footsteps: Evolutionary dynamics with the Wright-Fisher model

In which we use the Wright-Fisher model to demonstrate natural selection in action, and show how being the “fittest” doesn’t always mean that you will “win”. Think Homer Simpson winning a game of musical chairs.

Epilogue: Because breaking up is hard to do

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