Dynamical Modeling for Systems Biology with Python

Biological systems are complex and dynamic, making it difficult to predict how cells and pathways behave under changing conditions. Understanding these systems requires moving beyond static diagrams and into the realm of active mathematical representations. This text-based course teaches you how to translate biological mechanisms into dynamic, simulating models that can predict real-world cellular behavior. You will start with the fundamental terminology of systems biology and chemical kinetics, then progress to building and simulating ordinary differential equation (ODE) models. Using modern Python scientific libraries, you will learn to write clean, maintainable simulation code, analyze system stability, and fit models to experimental data. What you'll learn: - Understand the core mathematical concepts behind dynamical modeling in biology - Build kinetic models of biochemical reactions using rate laws and mass action - Simulate dynamic cellular processes using modern Python scientific packages and type-hinted code - Analyze system properties including steady states, feedback loops, and sensitivity - Apply basic parameter estimation techniques to align models with biological data - Explore modern open standards for sharing and reproducing computational models This course begins with essential concepts and step-by-step mathematical definitions before moving into practical code implementations and analysis techniques. It is designed for students, researchers, and programmers who are new to computational biology and want a solid, beginner-friendly foundation in modeling. Start modeling biological complexity and gain deeper insights into cellular dynamics today.