Quantitative Biology

Mathematical Modeling and Computation
Erscheinungsjahr 2026
ISBN: 978-0-443-27452-7
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 191 mm x 235 mm, Gewicht: 450 g

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.

Autoren/Hrsg.

Ogueda-Oliva, Alonso

Seshaiyer, Padmanabhan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

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

Über Autor(innen)

Ogueda-Oliva, Alonso
Alonso Oliva Ogueda holds a Master's degree in Mathematics from the Universidad Técnica Federico Santa María (2021) and a Mathematical Engineering degree from Universidad Técnica Federico Santa María (2019). He has worked on a variety of projects involving development of mathematical/statistical algorithms, data analysis, data science and engineering and Cloud computing.

Seshaiyer, Padmanabhan
Dr. Padmanabhan Seshaiyer is a tenured Professor of Mathematical Sciences at George Mason University and serves as the Director of the STEM Accelerator Program in the College of Science as well as the Director of COMPLETE (Center for Outreach in Mathematics Professional Learning and Educational Technology) at George Mason University in Fairfax, Virginia. His research interests are in the broad areas of computational mathematics, computational data science, scientific computing, computational biomechanics, design and systems thinking, entrepreneurship and STEM education. During the last decade, Dr. Seshaiyer initiated and directed a variety of educational programs including graduate and undergraduate research, K-12 outreach, teacher professional development, and enrichment programs to foster the interest of students and teachers in STEM at all levels.