Abstract: Models based on Ordinary Differential Equations (ODEs) or their stochastic counterparts are routinely applied in systems biology to understand, analyze and predict properties and behaviour of biomolecular dynamical systems, such as enzymatic networks. However, the system's properties and behaviour depend strongly on the modeling equations and the assumptions we make about the system. How robust are predictions with respect to the assumptions? What can we say about the mathematical relationship between different models of the same biological system?
In recent years, model reduction -- how do we simplify a complex model without loosing essential properties of the model -- has been a topic in stochastic and deterministic reaction network theory. In this talk, I will discuss model reduction and some recent results on model reduction, based on deterministic as well as stochastic systems, at steady state, as well as out of steady state.