  We outline a modeling and optimization strategy for investigating dynamic
metabolic engineering interventions. Our framework is particularly useful at
the early stages of research and development, often constrained by limited
knowledge and experimental data. Elucidating a priori optimal trajectories of
manipulatable intracellular fluxes can guide the design of suitable control
schemes, e.g., cyber(ge)netic or in-cell approaches, and the selection of
appropriate actuators, e.g., at the transcriptional or post-translational
levels. Model-based dynamic optimization is proposed to predict optimal
trajectories of target manipulatable intracellular fluxes. A challenge emerges
as existing models are often oversimplified, lacking insights into metabolism,
or excessively complex, making them difficult to build and implement. Here, we
use surrogates derived from steady-state solutions of constraint-based
metabolic models to link manipulatable intracellular fluxes to the process
exchange rates of structurally simple hybrid dynamic models. The latter can be
conveniently used in optimal control problems of metabolism. As a proof of
concept, we apply our method to a reduced metabolic network of
$\textit{Escherichia coli}$ considering two different scenarios of dynamic
metabolic engineering.
