Full Description
Quantitative Biology introduces and implements 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 in the book combine mathematical foundations, statistical reasoning, and computational thinking, with application in compartmental, agent-based, bio image, biological interaction, and neural network modeling, as well as machine learning, parameter identification, and more, with a later chapter considering 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, helping 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.
Contents
1. Computational Thinking for Mathematical Biology
2. Modeling and Computation for Biological Interactions
3. Parameter identification for Biological Systems
5. From Deterministic to Predictive Modeling
6. Classification Algorithms for Modeling Biological Systems
7. Data-driven approaches for Bioimage Processing
8. Physics Informed Neural Networks for Biological Dynamics
9. Data-driven Optimal Control in Mathematical Biology
10. Data-driven approaches for real-world Societal Challenges
