Study On Structured Model For2-KGA Mixed Culture

Production of Vitamin C (VC) is commonly carried out withtwo-step fermentation process. With the competition of VC marketbecoming more and more fierce, optimal control of such process andimprovement of VC production have come into focus in recent years. Fordecades, great progress has been made in the first step fermentation, sothat researchers are mainly focusing on improvement of2-KGA(2-keto-L-gulonic acid) productivity in the second step, where L-sorboseis converted via mixed culture of Bacillus megaterium (called bigbacterium in VC industry) and Ketogulonicigenium vulgare (called smallbacterium). Mathematic modeling of2-KGA mixed culture serves as thefoundation of optimal control, which is the main concern of the currentstudy. First, the interaction mechanism between the two microorganismsis investigated. Based on the metabolic pathway analysis in K.vulgare, amacrokinetic model is constructed, where, a first-order closed-loopregulator model is introduced to describe the induction of the enzymepool responsible for the growth of K.vulgare at the beginning of thecultivation. The model describes the stoichiometric balance of carbonsource, ATP, NADH and pyruvate. The specific substrate consumptionrate and the specific growth rate are then coupled into a bioreactor modelsuch that the relationship between the substrate feeding rate and the mainstate variables, i.e., the concentrations of the two microorganisms,substrate and product, the medium volume, is set up. Moreover, Differential Evolution was applied for model parameter identification.Experimental validation results demonstrate that the model can describethe cell growth and the2-KGA production with reasonable accuracy.Finally, interspecific-intraspecific signal transduction pathway isanalyzed, signal interact process between B.megaterium and K.vulgare isinvestigated for2-KGA mixed culture, the microbial interaction betweensuch two bacteria get further understood, this provides a basis for furthersignal transduction networks modeling and simulation.