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As the key precursor for l -ascorbic acid synthesis, 2-keto- l -gulonic acid (2-KGA) is widely produced by the mixed culture of Bacillus megaterium and Ketogulonicigenium vulgare . In this study, a Bayesian combination of multiple neural networks is developed to obtain accurate prediction of the product formation. The historical batches are classified into three categories with a batch classification algorithm based on the statistical analysis of the product formation profiles. For each category, an artificial neural network is constructed. The input vector of the neural network consists of a series of time-discretized process variables. The output of the neural network is the predicted product formation. The training database for each neural network is composed of both the input–output data pairs from the historical bathes in the corresponding category, and all the available data pairs collected from the batch of present interest. The prediction of the product formation is practiced through a Bayesian combination of three trained neural networks. Validation was carried out in a Chinese pharmaceutical factory for 140 industrial batches in total, and the average root mean square error ( RMSE ) is 2.2% and 2.6% for 4 h and 8 h ahead prediction of product formation, respectively.

A dynamic model called hybrid cybernetic model (HCM) based on structured metabolic network is established for simulating mammalian cell metabolism featured with partially substitutable and partially complementary consumption patterns of two substrates, glucose and glutamine. Benefiting from the application of elementary mode analysis (EMA), the complicated metabolic network is decomposed into elementary modes (EMs) facilitating the employment of the hybrid cybernetic framework to investigate the external and internal flux distribution and the regulation mechanism among them. According to different substrate combination, two groups of EMs are obtained, i.e., EMs associated with glucose uptake and simultaneous uptake of glucose and glutamine. Uptake fluxes through various EMs are coupled together via cybernetic variables to maximize substrate uptake. External fluxes and internal fluxes could be calculated and estimated respectively, by the combination of the stoichiometrics of metabolic networks and fluxes through regulated EMs. The model performance is well validated via three sets of experimental data. Through parameter identification of limited number of experimental data, other external metabolites are precisely predicted. The obtained kinetic parameters of three experimental cultures have similar values, which indicates the robustness of the model. Furthermore, the prediction performance of the model is successfully validated based on identified parameters.

A macrokinetic model describing mycelium growth and product formation in fed-batch fermentation of Penicillium chrysogenum is proposed on the basis of intracellular metabolic pathway networks found in the literature. The model is composed of the stoichiometric balance equations for carbon source, ATP, NADH and pyruvate. A regulator model is used as an auxiliary part to simulate the lag phase at the beginning of product synthesis. Combining the macrokinetic model with the bioreactor model, one yields the relationship between manipulating variables, i.e., the substrate feeding rate, and the other state variables such as biomass, substrate and product concentrations. The rolling parameter identification is then applied to online identify the most sensitive model parameters dealing with the model mismatch caused by time-variant kinetic changes. Model validation was performed with the data of 22 batches from industrial-scale penicillin cultivation. Finally, the predictions of the product concentration, up to 24 h ahead, are carried out with an average prediction error of less than 3.5%.

A cell cycle model is proposed for methylotrophic yeast Pichia pastoris grown on glycerol during fed-batch cultivation. Morphological differentiation of cells, such as unbudded daughter cell, unbudded parent cell and budding cell, is depicted by the model. During the cyclic growth, cells in different cycling period are assumed to undergo sequential shifting dominantly. The input of the cell cycle model is the specific growth rate, which is calculated from the macrokinetic model proposed previously. The cell cycle related variables, such as the fraction of budding cells and the cell density are then simulated. Model validation is carried out with the experimental data of off-line assays.

A macrokinetic model is proposed to simulate the growth and metabolism of myeloma cell line in batch and fed-batch cultures. The model describes glycolysis, glutaminolysis, the TCA cycle, respiratory chain, formation of metabolites, as well as cell growth and death. In particular, metabolic shift in the glucose utilization is considered in response to the change of glycolysis rate during the cultivation. A closed loop regulator is introduced to approximate the induction of enzyme pool during lag phase after inoculation. Based on the model, the specific consumption rate of substrate, the specific growth rate, the specific acetyl CoA formation rate as well as the specific oxygen uptake rate are estimated. The specific substrate consumption rate and the specific growth rate are then coupled into a bioreactor model, which delivers the key variables, i.e., cell density, substrate and metabolite concentrations. Batch and fed-batch cultivations were used to validate the model.