Quantitative Biology provides quantitative and data-driven approaches for analyzing biological and bio-inspired systems, covering the foundations of mathematical modeling, analysis, and computation. The book presents a practical mix of both theory and computation for a variety of biological applications, with tied-in, engaging project activities, instruction, programming language, and technological tools. Modeling approaches combine mathematical foundations, statistical reasoning, and computational thinking, with applications in compartmental, agent-based, bio image, biological interaction, and neural network modeling, as well as machine learning, parameter identification, and applications across societal challenges.
Each chapter includes exposure to models and modeling, a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style that helps readers go beyond replication models and into prediction and data-driven discovery. A companion website also features interactive code to accompany projects across each chapter.
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Table of Contents
About the BookForeword
Acknowledgement
1. Computational Thinking for Mathematical Biology
2. Modeling and Computation for Biological Interactions
3. Understanding Spread of Infection and Epidemic Dynamics
4. Modeling, Analysis and Computation in Epidemiology
5. Foundations of Optimal Control Theory for Biological Systems
6. Incorporating spatial dynamics into biological systems
7. From Deterministic to Predictive Modeling
8. Data-Driven Classification for Biological Applications through Machine Learning
9. Physics Informed Neural Networks for Predicting Biological Dynamics
