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 non-quantitative 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 around 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 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 contains a number of exercises for the reader to engage with


• Available for qualified instructors, lecture slides and animations to cover the key arguments and derivations in each chapter, as well as example exam questions.

Table of Contents:

1. On the road
2. Outbreaks
3. Building a Better car
4. Survival of the Fastest
5. Emergence
6. Growing Too Big
7. Shrinking Too Small
8. Time and Chance
9. Is it Normal?
10. Lather, Rinse, Repeat
11. Agents of Change
12. Ducks in a Row
13. Life on a Tree
14. Life in a Net
15. Scale
16. Bits
Glossary
Index

Introduction to dynamical models: Infectious diseases and physical examples. 2. Outbreaks. Modeling an infectious disease outbreak with differential equations. 3. Building a better cat. Models in science: the predator-prey systems and chaos. 4. Survival of the fastest. Modeling competition between species and between cells. 5. Emergence. Modeling biochemistry with differential equations, emergent properties and genetic dominance 6. Growing too big. When models break down: Full-cell metabolic models 7. Shrinking too small. When models break down, Part 2: Noise in biochemical systems 8. Time and chance. Probability and random variables. 9. Is it normal? Sampling, statistics, and the central limit theorem. 10. Lather, rinse, repeat. Computer programming: A gentle introduction. 11. Agents of Change. Agent-based models of genetic drift 12. Ducks in a row: Bioinformatics: Algorithmic approaches to biological data. 13. Life on a tree. Phylogenetics: Bringing together probability distributions, differential equations, computation and evolution 14. Life in a net. Network biology: Tools for understanding complex interacting systems from the cell to an ecosystem 15. Scale. Metabolic rate, nutrient exchange, body size and fractal geometry 16. Bits. Life as an information transfer process