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
Written in an accessible, narrative styleIncludes hands-on analyses with open-source toolsIntegrates biology across spatial and temporal scalesLinks to a course website with interactive toolsBrings biological education into the "data science" eraEach chapter includes a variety of exercises designed to actively engage the readerLecture slides and animations to cover the key arguments and derivations in each chapter, as well as example exam questions, are available for qualified instructors.
Biology has become a data-driven science. Practicing biologists have no choice but to use computational approaches and statistics in their research. This book, based around a series of hands-on analyses conducted with open-source tools, provides students whose background is in biology with an introduction to modeling biological systems.
Preface. Acknowledgments.
Chapter 1: On the Road: Dynamical Models of
Infectious Diseases and Physical Systems.
Chapter 2: Outbreaks: Modeling an
Infectious Disease Outbreak with Differential Equations.
Chapter 3: Building
a Better Cat: Building a PredatorPrey Model and Chaos.
Chapter 4: Survival
of the Fastest: Modeling Competition between Species and between Cells.
Chapter 5: Emergence: Genetic Dominance as an Emergent Property of
Biochemical Models.
Chapter 6: Growing Too Big: Full-Cell Metabolic Models.
Chapter 7: Shrinking Too Small: Noise in Biochemical Systems.
Chapter 8: Time
and Chance: Probability and Random Variables.
Chapter 9: Is It Normal?:
Sampling, Statistics, and the Central Limit Theorem.
Chapter 10: Lather,
Rinse, Repeat: A Gentle Introduction to Computer Programming.
Chapter 11:
Agents of Change: Computational Models of Genetic Drift.
Chapter 12: Ducks in
a Row: Bioinformatics and Algorithmic Approaches to Biological Data.
Chapter
13: Life on a Tree: Phylogenetics.
Chapter 14: Life in a Net: Network Tools
for Modeling Complex Systems from a Cell to an Ecosystem.
Chapter 15: Scale:
Metabolic Rate, Body Size, and Fractal Geometry.
Chapter 16: Bits: Life as an
Information Transfer Process. Glossary. Index.
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.
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