Quantitative Biology by Conant, Gavin;

Short description:

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.

Long description:

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.

• Written in an accessible, narrative style








• Includes hands-on analyses with open-source tools








• Integrates biology across spatial and temporal scales








• Links to a course website with interactive tools








• Brings biological education into the "data science" era








• 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.

Table of Contents:

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 Predator–Prey 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.