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Microorganisms have developed complex regulatory features controlling theirreaction and internal adaptation to changing environments. When modeling theseorganisms we usually do not have full understanding of the regulation and relyon substituting it with an optimization problem using a biologically reasonableobjective function. The resulting constraint-based methods like the FluxBalance Analysis (FBA) and Resource Balance Analysis (RBA) have proven to bepowerful tools to predict growth rates, by-products, and pathway usage forfixed environments. In this work, we focus on the dynamic enzyme-cost FluxBalance Analysis (deFBA), which models the environment, biomass products, andtheir composition dynamically and contains reaction rate constraints based onenzyme capacity. We extend the original deFBA formalism to include storagemolecules and biomass-related maintenance costs. Furthermore, we present anovel usage of the receding prediction horizon as used in Model PredictiveControl (MPC) in the deFBA framework, which we call the short-term deFBA(sdeFBA). This way we eliminate some mathematical artifacts arising from theformulation as an optimization problem and gain access to new applications inMPC schemes. A major contribution of this paper is also a systematic approachfor choosing the prediction horizon and identifying conditions to ensuresolutions grow exponentially. We showcase the effects of using the sdeFBA withdifferent horizons through a numerical example.

Dynamic modeling of enzyme controlled metabolic networks using a receding time horizon