COPASI is a popular open source systems biology software for simulation and analysis of biochemical reaction networks. Beyond the basic functions for calculating steady states and time courses, COPASI is equipped with a number of higher-level analyses that can be used individually or combined to provide powerful characterizations of models. This includes mass conservation analysis, sensitivity analysis, nonlinear optimization, and time-scale separation analysis. COPASI is also able to use several modeling frameworks, including the popular differential equation approach, but also various stochastic simulations frameworks, including stochastic differential equations. Discrete events can be added to any simulation, and these can be used not only to represent specific features of models, but are also useful to create powerful monitoring functions that act like digital probes for simulations. The most widely used feature of COPASI is parameter estimation, which is supported by a large number of optimization algorithms and support for parameter identifiability analysis. Importantly, COPASI supports community standards, such as SBML, SED-ML and formal model annotation. These features are essential for portability, reproducibility and interoperability of models. I will illustrate COPASI’s features with a few specific applications, such as the modeling of whole-body iron homeostasis.