  Mechanistic growth models play a major role in bioprocess engineering,
design, and control. Their reasonable predictive power and their high level of
interpretability make them an essential tool for computer aided engineering
methods. Additionally, since they contain knowledge about cell physiology, the
parameter estimates provide meaningful insights into the metabolism of the
microorganism under study. However, the assumption of time invariance of the
model parameters is often violated in real experiments, limiting their capacity
to fully explain the observed dynamics. In this work, we propose a framework
for identifying such violations and producing insights into misspecified
mechanisms. The framework achieves this by allowing kinetic and process
parameters to vary in time. We demonstrate the framework's capabilities by
fitting a hybrid model based on a simple mechanistic growth model for E. coli
with data generated in-silico by a much more complex one and identifying
missing kinetics.
