  Microorganisms have developed complex regulatory features controlling their
reaction and internal adaptation to changing environments. When modeling these
organisms we usually do not have full understanding of the regulation and rely
on substituting it with an optimization problem using a biologically reasonable
objective function. The resulting constraint-based methods like the Flux
Balance Analysis (FBA) and Resource Balance Analysis (RBA) have proven to be
powerful tools to predict growth rates, by-products, and pathway usage for
fixed environments. In this work, we focus on the dynamic enzyme-cost Flux
Balance Analysis (deFBA), which models the environment, biomass products, and
their composition dynamically and contains reaction rate constraints based on
enzyme capacity. We extend the original deFBA formalism to include storage
molecules and biomass-related maintenance costs. Furthermore, we present a
novel usage of the receding prediction horizon as used in Model Predictive
Control (MPC) in the deFBA framework, which we call the short-term deFBA
(sdeFBA). This way we eliminate some mathematical artifacts arising from the
formulation as an optimization problem and gain access to new applications in
MPC schemes. A major contribution of this paper is also a systematic approach
for choosing the prediction horizon and identifying conditions to ensure
solutions grow exponentially. We showcase the effects of using the sdeFBA with
different horizons through a numerical example.
