Product details
- ISBN 9781041170150
- Weight: 570g
- Dimensions: 210 x 280mm
- Publication Date: 18 Feb 2026
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
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Biology at all scales has become a data-driven science, with large-scale datasets driving fields from population genomics to ecology. Practicing biologists have no choice but to use computational approaches, statistics, modeling, and other data science tools in their research. However, undergraduate biology education still primarily focuses on nonquantitative descriptions. This book provides students whose background is in biology with an introduction to modeling biological systems using mathematical, computational, and statistical tools. It is based on a series of hands-on analyses conducted with open-source tools that allow the students to discover for themselves emergent properties of biological systems that are not evident without using model-based approaches. The goal of this book is to provide a "turn-key" introductory quantitative biology course suitable for all biology students. The book provides the narrative for the analyses and discussions to be done in class, with support from the included website, slides, and test material.
Key Features
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- Written in an accessible, narrative style
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- Includes hands-on analyses with open-source tools
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- Integrates biology across spatial and temporal scales
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- Links to a course website with interactive tools
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- Brings biological education into the "data science" era
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- Each chapter includes a variety of exercises designed to actively engage the reader
- Lecture slides and animations to cover the key arguments and derivations in each chapter, as well as example exam questions, are available for qualified instructors.
Gavin Conant worked as a researcher in evolutionary and computational biology for more than 25 years and has authored or coauthored more than 80 peer-reviewed scholarly articles, as well as book chapters and articles for the popular press. His research spans bioinformatic algorithm development, data visualization, evolutionary biology, metabolic modeling, parallel computing, and microbial ecology.
