Search

In the two-step vitamin C fermentation process, its precursor 2-keto-L-gulonic acid was synthesized from L-sorbose by mixed culture of Gluconobacter oxydans and Bacillus megaterium. The interaction between Gluconobacter oxydans and Bacillus megaterium remains unclear and it is a challenge to mathematically model the mixed growth of these two strains. The Monod-type equations were previously proposed to describe the coupled growth of Gluconobacter oxydans and Bacillus megaterium. However, in this study, we modeled the interaction of these two strains in a macroscopic view by introducing the population theory. Taking account of the fact that the density or concentration of Gluconobacter oxydans or Bacillus megaterium was hardly to measure accurately in the mixed culture broth, the data of concentrations of the substrate and product were used to indirectly investigate the relation between these two strains. Three batch experiments were used to validate our model. And according to the values of identified parameters, the type of interaction between Gluconobacter oxydans and Bacillus megaterium was concluded to be predation, where Gluconobacter oxydans was predator, and Bacillus megaterium was prey.

This article presents a modeling approach for industrial 2-keto-L-gulonic acid (2-KGA) fed-batch fermentation by the mixed culture of Ketogulonicigenium vulgare (K. vulgare) and Bacillus megaterium (B. megaterium). A macrokinetic model of K. vulgare is constructed based on the simplified metabolic pathways. The reaction rates obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between substrate feeding rates and the main state variables, e.g., the concentrations of the biomass, substrate and product, is constructed. A differential evolution algorithm using the Lozi map as the random number generator is utilized to perform the model parameters identification, with the industrial data of 2-KGA fed-batch fermentation. Validation results demonstrate that the model simulations of substrate and product concentrations are well in coincidence with the measurements. Furthermore, the model simulations of biomass concentrations reflect principally the growth kinetics of the two microbes in the mixed culture.

Prediction of protein subcellular location is a meaningful task which attracted much attention in recent years. A lot of protein subcellular location predictors which can only deal with the single-location proteins were developed. However, some proteins may belong to two or even more subcellular locations. It is important to develop predictors which will be able to deal with multiplex proteins, because these proteins have extremely useful implication in both basic biological research and drug discovery. Considering the circumstance that the number of methods dealing with multiplex proteins is limited, it is meaningful to explore some new methods which can predict subcellular location of proteins with both single and multiple sites. Different methods of feature extraction and different models of predict algorithms using on different benchmark datasets may receive some general results. In this paper, two different feature extraction methods and two different models of neural networks were performed on three benchmark datasets of different kinds of proteins, i.e. datasets constructed specially for Gram-positive bacterial proteins, plant proteins and virus proteins. These benchmark datasets have different number of location sites. The application result shows that RBF neural network has apparently superiorities against BP neural network on these datasets no matter which type of feature extraction is chosen.

A set of kinetic models have been developed for the production of 2-keto-L-gulonic acid from L-sorbose by a mixed culture of Gluconobacter oxydans and Bacillus megaterium. A metabolic pathway is proposed for Gluconobacter oxydans, and a macrokinetic model has been developed for Gluconobacter oxydans, where the balances of some key metabolites, ATP and NADH are taken into account. An unstructured model is proposed for concomitant bacterium Bacillus megaterium. In the macrokinetic model and unstructured model, the mechanism of interaction between Gluconobacter oxydans and Bacillus megaterium is investigated and modeled. The specific substrate uptake rate and the specific growth rate obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between the substrate feeding rate and the main state variables, such as the medium volume, the biomass concentrations, the substrate, and the is set up. A closed loop regulator model is introduced to approximate the induction of enzyme pool during lag phase after inoculation. Experimental results demonstrate that the model is able to describe 2-keto-L-gulonic acid fermentation process with reasonable accuracy.

The present work is initiated to investigatewhether a defined culture comprising a mixture of threeyeast species, Kluyveromyces marxianus, Saccharomyces cer-evisiae, and Pichia stipitis can ferment a mixture of sugars toproduce bioethanol at rates higher than those achieved bypure cultures of the same. For this purpose, we developmodels of single species based on the hybrid cyberneticmodel framework, and simulate fermentations in the mixedculture by combining individual models. An underlyingassumptionisthatthebehaviorofeachspeciesisdeterminedonly by the common environment independently of thepresence and metabolism of other species. Model perfor-mance is thoroughly assessed using the experimental dataavailable in the literature. The dynamic behavior of mixedcultures in mixed culture experiments are accurately pre-dicted by the model reflecting faithfully the simultaneous/sequentialuptakepatternsofmixedsubstrates.Thismodelisthen used to investigate performance of various possiblereactor configurations. With the foregoing species of organ-isms, mixed culture itself does not lead to a significantincrease of bioethanol productivity. Rather, the modelshows that substantial improvement is acquired by sequen-tial use of different, properly chosen organisms duringfermentation. Thus, the successive use of K. marxianusand P. stipitis is shown to increase bioethanol productivityup to about 58% in comparison to fermentation by singlespecies alone. Biotechnol. Bioeng.Biotechnol. Bioeng. 2012;109: 1508–1517. 2012 Wiley Periodicals, Inc.KEYWORDS: hybrid cybernetic model (HCM); elementarymodes (EMs); mixed culture; lignocellulose; bioethanolproductivity

Mixed culture fermentation of Bacillus megaterium and Gluconobacter oxydans is widely used to produce 2-keto- l -gulonic acid (2-KGA), a key precursor for l -ascorbic acid synthesis. For such mixed cultivation, kinetic modelling is difficult because the interactions between the two strains are not well known yet. In this paper, data-driven prediction of the product formation is presented for the purpose of better process monitoring. A rolling learning-prediction approach based on neural networks is practiced to predict 2-KGA formation. Techniques associated with the approach, such as the data pretreatment and the rolling learning-prediction mechanism, are given in more detail. The validation results by using the data from commercial scale 2-KGA cultivation indicate that the prediction error is less than 5% in the later phase of fermentation and the reliable prediction time span is 8 h. The robustness of the prediction approach is further tested by adding extra noises to the process variables.

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.

An oxygen transfer model was established for Pichia pastoris growing on glycerol and methanol in a stirred tank bioreactor and expressing a recombinant human serum albumin (rHSA). This was based on pseudo-steady state mass balance, where the volumetric O2 transfer coefficient, k L a, was estimated as a function of power input per unit volume and aeration rate. Under pseudo-steady state, the O2 transfer rate model matched the O2 uptake rate obtained from a previous macrokinetic model. This procedure was also applied to estimate biomass concentration by using the on-line rolling identification approach.

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.

Constant specific growth rate control in the methanol growth phase was investigated for the fed-batch cultivation of Pichia pastoris expressing recombinant human serum albumin (rHSA). The methanol feeding strategy was determined based on an earlier proposed macrokinetic model to maintain the specific growth rate at preset levels. The experimental results demonstrate that the control strategy of constant specific growth rate is more effective than that of constant feeding rate to maximize production. Furthermore, the most productive setpoint of the specific growth rate is found between 0.005 and 0.006 h−1, which yields protein concentrations higher than 5 g l−1 at 160 h. In addition, a setpoint of 0.008 h−1 is suggested as the upper limit for specific growth rate control for the given expression system. Copyright © 2005 Society of Chemical Industry

Pichia pastoris has been widely used as a host for the expression of heterologous proteins. This paper deals with the glycerol and methanol optimal feeding control in the cultivation of P. pastoris expressing recombinant human serum albumin. An earlier proposed macrokinetic model and a cell cycle model were used to determine the substrate feeding profiles. In the glycerol growth phase, the goal of substrate feeding control is to obtain high cell density as well as the desired level of the fraction of budding cells, while in the methanol growth phase, the goal of substrate feeding is to control the specific growth rate at various set-points. The effect of both glycerol and methanol feeding control strategies were evaluated experimentally. It was found that the productivity may be enhanced if the fraction of budding cells at the end of the glycerol growth phase is minimized and if the specific growth rate during the methanol phase controlled between 0.005 and 0.006 h-1.

A structured kinetic model, which takes account of major metabolic pathways of glycerol and methanol in Pichia pastoris, is presented. Based on the combined kinetic and bioreactor model, feeding profiles of methanol are detennined with the aim of maintaining constant specific growth rate during production stage. Compared with the decreasing type of specific growth rate resulted from constant feeding profile in the standard protocol, the constant specific growth rate is believed to be advantageous for improving the productivity. Experimental results indicate that simulations of biomass and protein concentration agree well with the measured data, and the specific growth rates were successfully controlled at various set points.

In mycelia cultivation, the moiphology of filamentous microorganisms changes greatly along with the cultivation time, which causes variations in mass transfer and metabolic behavior of the microorganisms. To give an insight into the morphology-associated time-variant process dynamics in a simple way, a cell age model is proposed for Penicillium chrysogenum fed-batch cultivation. With help of this model the average ages of the segregated cell populations may be estimated. The age model is further combined with a segregated penicillin model found in the literature. The combined model system seems to work well which is shown by the verification results with commercial penicillin fed-batch cultivations.

In baker's yeast continuous fermentation process, profit optimization is essentially the final target of all process control and scheduling measures. In this paper, an earlier proposed structured kinetic model for baker's yeast Saccharomyces cerevisiae was tested and modified with industrial fermentation data. With the support of the process model system, the profit function of the whole process is estimated This makes a further profit optimization possible.

The cell cycling model (CCM) for S. cerevisiae proposed earlier is modified and tested with our own experimental data. Although the original CCM was well verified in steady states and exponential growth with data available in literature, some discrepancies between model predictions and experiments were found for the dynamics of fed-batch culture. The redistribution pattern of the age distribution of daughter cells is suggested as cause of the model error. With an exponential type of redistribution, instead of the original linear one, the model behaviour in transients is improved. The modified model was verified with data of fraction of budding cells and cell number for five fed-batch cultivations. The model agreed well with the experimental data. The simulation results suggest that the cell cycling process indeed is essentially in a pseudo-steady state during fed-batch cultivation, as was assumed in the model. Due to the strong correlation between the quality of baker's yeast and the state of the population in the cell cycling process, the model was applied to optimize the feeding rate of a fedbatch process with consideration of final product quality. The optimal feeding was used succesfully in a laboratory experiment, which demonstrates the validity of the model.

A cell cycling model for unequal budding yeast Saccharomyces cerevisiae is proposed and verified by steady state data from experiments available in the literature. This model can be used to determine the relative fraction of the cells in any cycling phase or with any genealogical age during fermentation. As the quality of yeast is strongly influenced by the cycling process, the model could therefore be used to control the quality of the harvested yeast cells. The input of the cell cycling model is the specific growth rate μ, which is obtained from a metabolic model for S. cerevisiae proposed earlier. With this extended model system not only the quality control, but also the whole economical profit optimization can be carried out. Simulations were done to optimize the profit of a commercial scale baker's yeast production process by manipulating substrate feeding rate and substrate concentration under different aeration rates, fermentation periods and other conditions applied in industry.

A macrokinetic model employing cybernetic methodology is proposed to describe mycelium growth and penicillin production. Based on the primordial and complete metabolic network of Penicillium chrysogenum found in the literature, the modeling procedure is guided by metabolic flux analysis and cybernetic modeling framework. The abstracted cybernetic model describes the transients of the consumption rates of the substrates, the assimilation rates of intermediates, the biomass growth rate, as well as the penicillin formation rate. Combined with the bioreactor model, these reaction rates are linked with the most important state variables, i.e., mycelium, substrate and product concentrations. Simplex method is used to estimate the sensitive parameters of the model. Finally, validation of the model is carried out with 20 batches of industrial-scale penicillin cultivation.

A simple kinetic model is developed to describe the dynamic behavior of myeloma cell growth and cell metabolism. Glucose, glutamine as well as lysine are considered as growth limiting substrates. The cell growth was restricted as soon as the extracellular lysine is exhausted and then intracellular lysine becomes a growth limiting substrate. In addition, a metabolic regulator model together with the Monod model is used to deal with the growth lag phase after inoculation or feeding. By using these models, concentrations of substrates and metabolites, as well as densities of viable and dead cells are quantitatively described. One batch cultivation and two fed-batch cultivations with pulse feeding of nutrients are used to validate the model.

A multi-staged population balance model is proposed to describe the cell cycle dynamics of myeloma cell cultivation. In this model, the cell cycle is divided into three stages, i.e., G1, S, and G2M phases. Both DNA content and cell volume are used to differentiate each cell from other cells of the population. The probabilities of transition from G1 to S and division of G2M are assumed to be dependent on cell volume, and transition probability from S to G2M is determined by DNA content. The model can be used to simulate the dynamics of DNA content and cell volume distributions, phase fractions, and substrate and byproduct concentrations, as well as cell densities. Measurements from myeloma cell cultivations, especially the FACS data with respect to DNA distribution and cell fractions in different stages, are employed for model validation.

A two pathway spatiotemporal model is proposed to describe the function of tonic suppressed-by-contrast cells of the cat retina. The model is able to describe the experimentally determined responses of such neurons to drifting sinusoidal gratings. It is also able to predict their responses to alternating sinusoidal gratings and flashing or moving spots of light, and these predictions resemble experimental observations, at least qualitatively. The model is physiologically plausible, it can be used to summarize the dynamic responses of the tonic suppressed-by-contrast cells of the cat and potentially to account for the responses of the suppressed-by-contrast cells of other species.

The work outlined in this paper is towards developing a portable, economy blood glucose meter for self-monitoring of blood glucose, which has high performance-price ratio with multifunction and is attractive to the average people. Its functions include blood glucose concentration measurement, LCD display, historical records restore, calendar/timer. It enables the patients with diabetes to detect blood glucose with a glucose oxidase electrode independently at home. This paper will outline hardware configuration in the implementation and software management. Especially, graphic trend curve on LCD allows individuals to easily see how actual blood glucose readings compare with historical records stored in the meter and show how individuals stay within their specific target range so that they can determine how well they are controlling their diabetes. Experimental results indicate that this device is easy-to-use and suitable for home blood glucose monitoring for patients.

A macrokinetic model for Pichia pastoris expressing recombinant human serum albumin is proposed. The model describes the balances of some key metabolites, ATP and NADH, during glycerol and methanol metabolism. In the glycerol growth phase, the metabolic pathways mainly include phosphorylation, glycolysis, tricarboxylic acid cycle, and respiratory chain. In the methanol growth phase, methanol is oxidized to formaldehyde at first. Then, while a part of formaldehyde is oxidized to formate, the rest is condensed with xylulose-5-monophosphate to form glyceraldehyde-3-phosphate, and further assimilated to form cell constituents. The metabolic pathways following glyceraldehyde-3-phosphate were assumed to be similar to those in the glycerol growth phase. Based on the model, the macrokinetic bioreaction rates such as the specific substrate consumption rate, the specific growth rate, the specific acetyl-CoA formation rate as well as the specific oxygen uptake rate are obtained. The specific substrate consumption rate and the specific growth rate are then coupled into a bioreactor model such that the relationship between substrate feeding rates and the main state variables, i.e., the medium volume, the concentrations of the biomass, the substrate, and the product, is set up. Experimental results demonstrate that the model can describe the cell growth and the protein production with reasonable accuracy.

A macrokinetic model for a myeloma cell line is proposed. The model describes the dynamic balances of lactate, alanine, ATP and NADH during the metabolsim of glucose, glutamine and other amino acids. The metabolic pathways mainly include glycolysis, glutaminolysis, the trcicarboxylic acid cycle, the formation and utilization of amino acids, the respiratory chain, cell growth and cell death. The metabolic shift of glucose is especially considered because of a change in the rate of glycolysis. Thus the model functions in three modes to describe the behaviour of the myeloma cell line. On the basis of this model the macrokinetic bioreaction rates such as the specific substrate consumption rate, 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 such that the key variables, i.e., the cell density, the substrate and metabolite concentrations, are obtained. Experiments with batch and fed-batch cultures of a myeloma cell line (X63-Ag8.653) were used to validate the model. The prediction of the model was simulated by the rolling prediction approach.