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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of figures - p. xiii 8. List of tables - p. xvii 9. Biography - p. xix 10. Foreword - p. xxi 11. Acknowledgments - p. xxvii 12. About the book - p. xxix 13. Chapter 1: Computational thinking for mathematical biology - p. 1 14. Chapter 2: Modeling and computation for biological interactions - p. 33 15. Chapter 3: Understanding spread of infection and epidemic dynamics - p. 75 16. Chapter 4: Modeling, analysis, and computation in epidemiology - p. 115 17. Chapter 5: Foundations of optimal control theory in mathematical biology - p. 147 18. Chapter 6: Incorporating spatial dynamics into biological systems - p. 181 19. Chapter 7: From deterministic to predictive modeling - p. 217 20. Chapter 8: Data-driven classification for biological applications through machine learning - p. 259 21. Chapter 9: Physics-informed neural networks for predicting biological dynamics - p. 297
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.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of figures - p. xiii 8. List of tables - p. xvii 9. Biography - p. xix 10. Foreword - p. xxi 11. Acknowledgments - p. xxvii 12. About the book - p. xxix 13. Chapter 1: Computational thinking for mathematical biology - p. 1 14. Chapter 2: Modeling and computation for biological interactions - p. 33 15. Chapter 3: Understanding spread of infection and epidemic dynamics - p. 75 16. Chapter 4: Modeling, analysis, and computation in epidemiology - p. 115 17. Chapter 5: Foundations of optimal control theory in mathematical biology - p. 147 18. Chapter 6: Incorporating spatial dynamics into biological systems - p. 181 19. Chapter 7: From deterministic to predictive modeling - p. 217 20. Chapter 8: Data-driven classification for biological applications through machine learning - p. 259 21. Chapter 9: Physics-informed neural networks for predicting biological dynamics - p. 297
