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Quantitative Biology,
Edition 1 Mathematical Modeling and ComputationEditors: By Alonso Ogueda-Oliva and Padmanabhan Seshaiyer
Publication Date:
29 Jan 2026
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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.
Key Features
- Introduces and demonstrates mathematical modeling, analysis, and computation for biological and bio-inspired systems
- Presents and instructs in computation for a variety of biological applications via engaging project activities, benchmark examples, and technology tools
- Offers insights into replicative models for biological systems, empowering prediction and data-driven discovery
- Includes a foundational instructional framework, benchmark applications, and numerical simulations with a literate programming guided style across all chapters
- Features a companion webpage with interactive code to accompany chapter projects
About the author
By Alonso Ogueda-Oliva, George Mason University, USA and Padmanabhan Seshaiyer, Professor of Mathematical Sciences, George Mason University, USA
About the Book
Foreword
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
Foreword
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
ISBN:
9780443274527
Page Count:
378
Retail Price
:
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Advanced undergraduate, graduate and PhD mathematics, biology, and data science students, among other disciplines