Mathematical Modeling in Life Sciences
A Practical Project-Based Approach
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Beschreibung
This textbook serves as an introduction to mathematical modeling of complex living systems. The authors introduce undergraduate students to modeling real-world biological processes with differential equations, dynamical systems, and data-driven analysis. Through hands-on projects and case studies, students will learn to apply mathematical methods and numerical simulations to understand complex living systems, from predator-prey dynamics to cancer virotherapy. Covering a wide range of topics and featuring practical MATLAB® simulations, the book also provides guidance on fitting models to real data and conducting parameter sensitivity analysis. The authors emphasize the power of mathematics and mathematical modeling in providing a deeper understanding of biological processes and in testing a variety of hypotheses. Through a practical project-based approach, readers will explore real-world scenarios to help apply theoretical knowledge in practical contexts. In addition, the steps of the modeling process including sensitivity analysis, numerical simulations, model predictions, and biological interpretations are provided. The book is well-suited for upper-undergraduate coursework or independent study and serves as a valuable resource for those aspiring to be researchers and practitioners in the life sciences.
In addition, this book:
- Equips readers with the tools and skills needed to explore and predict biological phenomena
- Provides step-by-step theoretical guidance and relevant MATLAB codes for students to learn to handle real-world data
- Introduces different modeling approaches using differential equations and dynamical systems
This textbook offers an introduction to the mathematical modeling of complex living systems. The authors guide undergraduate students in modeling ecological and biomedical problems using differential equations, dynamical systems, and data-driven methods. The modeling process—including assumption formulation, model construction, numerical simulation, sensitivity analysis, prediction, and biological interpretation—is presented step by step. Through hands-on projects and case studies, students learn to apply mathematical techniques and numerical simulations to study glucose–insulin regulation in diabetes, tumor–immune interactions in cancer therapy, and multi-species dynamics in Chesapeake Bay ecosystem. Together, these projects link mathematical theory to meaningful practice, helping students develop technical skills while fostering an appreciation of mathematics as a powerful tool for discovery and decision-making. Accompanied by student exercises and supported by MATLAB® code, the book also provides instruction on fitting models to real data and performing parameter sensitivity analysis. The book is well-suited for upper-undergraduate coursework or independent study and serves as a valuable resource for those aspiring to be researchers and practitioners in the life sciences.
Equips readers with the tools and skills needed to explore and predict biological phenomena beyond the classroom Provides step-by-step theoretical guidance and relevant MATLAB codes for students to learn to handle real-world data Introduces different modeling approaches using differential equations and dynamical systems
Autor*in
Iordanka Panayotova
Themen in »Mathematical Modeling in Life Sciences«
Mathematical Modeling Data-driven Dynamical Systems Sensitivity Analysis Equilibria Stability Biological Systems Parameter Sensitivity Analysis