2018/04/04 by Henning Lindhorst, Lindhorst, Henning, Alexandra-M. Reimers +3
Biochemistry, Genetics and Molecular Biology · Engineering · #Biofuel production and bioconversion #Enzyme Catalysis and Immobilization #FOS: Mathematics #Microbial Metabolic Engineering and Bioproduction #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.1804.01426
openalex publication_date 2018/04/04 · openalex created_date 2022/08/20 · openalex updated_date 2026/07/28
Microorganisms have developed complex regulatory features controlling their\nreaction and internal adaptation to changing environments. When modeling these\norganisms we usually do not have full understanding of the regulation and rely\non substituting it with an optimization problem using a biologically reasonable\nobjective function. The resulting constraint-based methods like the Flux\nBalance Analysis (FBA) and Resource Balance Analysis (RBA) have proven to be\npowerful tools to predict growth rates, by-products, and pathway usage for\nfixed environments. In this work, we focus on the dynamic enzyme-cost Flux\nBalance Analysis (deFBA), which models the environment, biomass products, and\ntheir composition dynamically and contains reaction rate constraints based on\nenzyme capacity. We extend the original deFBA formalism to include storage\nmolecules and biomass-related maintenance costs. Furthermore, we present a\nnovel usage of the receding prediction horizon as used in Model Predictive\nControl (MPC) in the deFBA framework, which we call the short-term deFBA\n(sdeFBA). This way we eliminate some mathematical artifacts arising from the\nformulation as an optimization problem and gain access to new applications in\nMPC schemes. A major contribution of this paper is also a systematic approach\nfor choosing the prediction horizon and identifying conditions to ensure\nsolutions grow exponentially. We showcase the effects of using the sdeFBA with\ndifferent horizons through a numerical example.\n