Computational Thinking for Life Scientists

Introduces fundamental computational ideas and concepts in a biological context, with real-world examples and exercises in Python.

Benny Chor (Author), Amir Rubinstein (Author)

9781107197244, Cambridge University Press

Hardback, published 8 September 2022

216 pages
25 x 17.5 x 1.5 cm, 0.56 kg

'The ability to extract quantitative information from data is an essential skill for the modern biologist. In order to maximize the benefit of programming, use of existing computational tools and effective collaboration with computational scientists, biologists must be able to 'think computationally' by gaining a more algorithmic and logical thinking. In their book, Benny Chor and Amir Rubinstein introduce fundamental computational concepts to life sciences students. Each chapter covers a distinct computational idea motivated by a concrete biological challenge. Questions embedded throughout each chapter and code examples provide hands-on practice. Similarly to the way in which chemistry is perceived as being essential to the biology curriculum, computational thinking should also be considered a part of the modern biologist's basic training. This excellent book is essential reading for undergraduate life sciences students.' Assaf Zaritsky, Ben-Gurion University of the Negev, Israel

Computational thinking is increasingly gaining importance in modern biology, due to the unprecedented scale at which data is nowadays produced. Bridging the cultural gap between the biological and computational sciences, this book serves as an accessible introduction to computational concepts for students in the life sciences. It focuses on teaching algorithmic and logical thinking, rather than just the use of existing bioinformatics tools or programming. Topics are presented from a biological point of view, to demonstrate how computational approaches can be used to solve problems in biology such as biological image processing, regulatory networks, and sequence analysis. The book contains a range of pedagogical features to aid understanding, including real-world examples, in-text exercises, end-of-chapter problems, colour-coded Python code, and 'code explained' boxes. User-friendly throughout, Computational Thinking for Life Scientists promotes the thinking skills and self-efficacy required for any modern biologist to adopt computational approaches in their research with confidence.

Introduction
Part I. Programming in Python: 1. Crash introduction to python
2. Efficiency matters – gentle intro to complexity
Part II. Sequences: 3. Sets dictionaries and hashing
4. Regular expressions and biological patterns
Part III. Networks: 5. Basic notions in graph theory
6. Shortest paths and breadth first search
7. Simulation of regulatory networks
Part IV. Images: 8. Digital images representation
9. Image processing
Part V. Limitations of Computing: 10. Mission impossible
11. Mission infeasible
Index.

Subject Areas: Genetics [non-medical PSAK]