Mathematical Modeling and Simulation in Systems Biology

Biological systems are inherently complex, but mathematical modeling and computational simulations provide clear frameworks to understand their dynamics. This text-based course guides you through translating biological mechanisms into quantitative mathematical models and running predictive simulations. You will start with core terminology, foundational calculus principles, and basic modeling concepts before advancing to practical modeling techniques. What you'll learn: Understand fundamental principles of mechanistic and deterministic modeling in biology; Formulate dynamic systems using ordinary differential equations; Apply modern computational workflows in Python to simulate cellular and molecular interactions; Model enzyme kinetics, gene regulatory networks, and biochemical pathways; Explore stochastic modeling techniques for capturing biological noise; Calibrate models with experimental data using parameter estimation techniques. The course begins with foundational concepts and definitions, gradually walking you through structured modeling frameworks, numerical methods, and modern simulation workflows with clear written explanations and text-based code exercises. This course is designed for beginners, students, and life science professionals looking to build computational modeling skills without requiring advanced mathematical prerequisites. Start reading today to build your computational modeling foundations in systems biology.