In conjunction with my business partner Alex Lancaster, we are very excited for this early release of a sample chapter from our forthcoming book Python For The Life Sciences. This book is written primarily for life scientists with little or no experience writing computer code, who would like to develop enough programming knowledge to be able to create software and algorithms that they can use to advance or accelerate their own research. These are probably scientists who are currently using spreadsheets and calculators to handle their data, but who have probably promised themselves that at some point when the opportunity arises, they will learn to write code. If this pretty well describes your situation, then your wait is over and the opportunity is knocking. This could very well be just the book you have been waiting for!
In short, this is the book that would like to have read when we were learning computational biology.
The aim of this book
The aim of this book is to teach the working biologist enough Python that he or she can get started using this incredibly versatile programming language in their own research, whether in academia or in industry. It also aims to furnish a Python foundation upon which the biologist can build by extrapolating from the broad set of Python fundamentals that the book provides.
What this book is not
This book is not another comprehensive guide to the Python programming language, nor is it intended to be a Python language reference. There are already plenty of those out there, and easily accessible online. For this reason, you will find that there are many (many) aspects and areas of the Python language that are not covered. In a similar vein, this book is not intended to be a life science primer for programmers and computer scientists.
A tour of computational biology beyond bioinformatics
This book is all about using computational tools to jumpstart your biological imaginations. We will show the reader the range of quantitative biology questions that can be addressed using just one language from a range of life sciences. The examples are deliberately eclectic and cover bioinformatics, structural biology, systems biology to modeling cellular dynamics, ecology, evolution and artificial life.
Like a good tour, these biological examples were deliberately chosen to be simple enough not to impede the reader’s ability to assimilate the Python coding principles being presented – but at the same time each scientific problem illustrates a simple, yet powerful principle or idea. By covering a wide variety of examples from different parts of biology, we also hope that the reader can identify common features between different kinds of models and data and encounter unfamiliar, yet useful ideas and approaches. We provide pointers and references to other code, software, books and papers where they can explore each area in greater depth.
We believe that exploring biological data and biological systems should be fun! We want to take you from the nuts-and-bolts of writing Python code, to the cutting edge as quickly as possible, so that you can get up and running quickly on your own creative scientific projects.
The sample chapter shows how to use Python to mine and understand data from transcription factor networks and you can get it here.
© The Digital Biologist