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Quantitative Biology

Mathematical Modeling and Computation

Specificaties
Paperback, blz. | Engels
Elsevier Science | 2026
ISBN13: 9780443274527
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Elsevier Science e druk, 2026 9780443274527
€ 107,79
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Samenvatting

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.

Specificaties

ISBN13:9780443274527
Taal:Engels
Bindwijze:Paperback

Inhoudsopgave

About the Book<br>Foreword<br>Acknowledgement<br>1. Computational Thinking for Mathematical Biology<br>2. Modeling and Computation for Biological Interactions<br>3. Understanding Spread of Infection and Epidemic Dynamics<br>4. Modeling, Analysis and Computation in Epidemiology<br>5. Foundations of Optimal Control Theory for Biological Systems<br>6. Incorporating spatial dynamics into biological systems<br>7. From Deterministic to Predictive Modeling<br>8. Data-Driven Classification for Biological Applications through Machine Learning<br>9. Physics Informed Neural Networks for Predicting Biological Dynamics
€ 107,79
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        Quantitative Biology