Yeast Fed-batch Culture Research Articles

Ethanol regulation in yeast fed-batch cultures by stabilizing extremum seeking

In this study, an original model-free stabilizing extremum seeking (STAB-ESC) strategy is applied to optimize yeast fed-batch culture productivity. To this end, the ethanol concentration is tracked at a low level, to approach the yeast growth optimal conditions. Overflow metabolism effects, resulting from the cell's limited respiratory capacity, are considered using a macroscopic model with discontinuous kinetics. The detrimental impact of input saturation on the proposed STAB-ESC stability is overcome using a state-dependent switching control strategy. The resulting methodology is illustrated with numerical results where the robustness of the controller to measurement noise is also highlighted.

Robust Model Predictive Control of a Vaccine Production Unit

In this paper, nonlinear model predictive controllers (NMPC) are proposed to optimize the biomass productivity of yeast fed-batch cultures. Their predictions are driven by a mechanistic model developed using a few industrial vaccine production data sets. The limited amount of data causes high parametric uncertainty levels and, to address this issue, a robust tube-based MPC is proposed and its robustness is assessed by a Monte-Carlo analysis, and compared to the classical MPC formulation.

Dynamic flux balance analysis for predicting biomass growth and ethanol production in yeast fed-batch cultures

In this study, a metabolic flux analysis (MFA) is firstly applied, at each time instant, for determining the range of admissible fluxes based on glucose and ammonium uptake rates and ethanol production rate estimated from measured concentrations in Saccharomyces cerevisiae fed-batch cultures. Given the similarity between the upper bound of the biomass production flux and its estimation through concentration measurements, a flux balance analysis (FBA), aiming at maximizing the biomass growth, is proposed and allows narrowing the admissible flux ranges within the metabolic network. Finally, after withdrawing the information corresponding to the ethanol production rate, original inequality constraints describing overflow metabolism of glucose are added to the FBA linear program in order to compensate for the lack of information regarding ethanol production. This final FBA model allows predicting biomass growth and ethanol production rates, which are consistent with the experimental measurements.

Adaptive extremum-seeking control applied to productivity optimization in yeast fed-batch cultures

In this study, we consider the problem of optimizing the productivity of fed-batch cultures of S. cerevisiae, which are characterized by strongly nonlinear kinetic models based on the bottleneck assumption of Sonnleitner and Käppeli [1986] and ethanol inhibition resulting from the fermentation of a possible excess of substrate feeding. In contrast with most published studies where the critical substrate level is assumed constant, we investigate the situation where this critical substrate level depends on the yeast respiratory capacity, and in turn on the oxygen and etahnol concentration in the culture medium. The challenge is thus to maintain the process at a high level of productivity by avoiding the accumulation of ethanol. To this end, an adaptive extremum seeking control scheme, coupled to an asymptotic observer, is developed based on Lyapunov stability arguments.

Robust adaptive control of yeast fed-batch cultures

Model-based control of bioprocesses is a difficult task due to the challenges associated with biological system modeling and the lack of on-line measurements. In this study, two robust controllers using minimal a priori process knowledge and minimal measurement information are designed to maximize biomass productivity in aerobic cultures of Saccharomyces cerevisiae. This latter objective can be achieved through the regulation of the ethanol concentration at a low constant value. The linearization of Sonnleitner’s model allows simple transfer function models to be derived, which describe the relation between the ethanol concentration, the substrate feed and an exponential disturbance – image of the substrate demand for cell growth – in the different operating (respirative and respiro-fermentative) regimes. The two controllers are based on these linear models and use a RST structure, but differ in the way the exponential growth disturbance is handled. In the first controller, the disturbance is represented by a linear model, whereas in the second controller, the disturbance is measured on-line via the oxygen transfer rate signal and a feedforward control action is used to cancel the disturbance effect on the ethanol concentration. Particular attention is paid to the robustification of the controllers to measurement noise, neglected high frequency dynamics and uncertain stoichiometry coefficients using the observer polynomial. Tests in simulation show the controller performance.

A practical robust control scheme for yeast fed-batch cultures – An experimental validation

Model-based control of bioprocess is a difficult task due to the challenges associated with bioprocess modeling and the lack of on-line measurements. In this study, a robust controller using minimal process knowledge and minimal measurement information is developed and applied to the ethanol regulation in cultures of Saccharomyces cerevisiae. A Youla parametrization is selected in order to reject the disturbance associated to the substrate demand for cell growth and to robustify the control scheme to unstructured uncertainties and measurement noise. The performance of this control scheme is illustrated in simulation and with real on-line experimental data.

MONITORING AND CONTROL OF A BIOPROCESS FOR MALARIA VACCINE PRODUCTION

Based on genetic manipulations, new strains of S. cerevisae are developed, which can be used for the production of pharmaceuticals. In this study, attention is focused on yeast fed-batch cultures dedicated to the production of a malaria vaccine. The efficient operation of this bioprocess requires on-line monitoring and regulation of the ethanol concentration at a low level (so as to maximize biomass productivity). This paper reports on the development of software sensors for the on-line reconstruction of biomass and ethanol, which are based on simple feedforward neural networks making only use of conventional bioprocess instrumentation (stirrer speed, base addition for pH regulation, etc.). This paper also discusses the design of a robust RST control strategy for regulating the ethanol concentration, which ensures setpoint tracking and asymptotic disturbance rejection. Robustification is achieved through the use of Youla parametrisation and on-line adaptation. This control strategy only requires the a priori knowledge about one yield coefficient and one on-line measurement sensor (i.e. an ethanol probe or the proposed software sensor). The software sensor and controller are tested successfully in real-case experimental runs.

Modeling and optimization of α-amylase production in a recombinant yeast fed-batch culture taking account of the cell cycle population distribution

A simple mathematical model describing the cell cycle dependency of rice α-amylase production by a recombinant yeast was constructed to investigate the efficiency of cell cycle population control. First, the effects of the glucose concentration and cultivation temperature on the specific growth rate, the specific production rate of rice α-amylase, and the distribution of the cell cycle population were studied under balanced growth conditions. On the basis of the results, parameter values for the mathematical model were then estimated. The proposed model was shown to be applicable for unbalanced as well as balanced growth phases. The optimal control strategy in respect of temperature and glucose concentration for maximum rice α-amylase production, taking into account the cell cycle population, was determined and the result was compared with that obtained by a simple mathematical model in which cell cycle distribution was not considered. Finally, the effect of the initial population of each cell cycle phase on the final amount of the product under optimal operational conditions was investigated. The simulation and experimental data coincided well with each other, and the model was used to optimize the control strategy for maximum α-amylase production.

On-line recognition of physiological state in a yeast fed-batch culture

Abstract The physiological states with respect to cell growth and ethanol production in a yeast fed-batch culture can be recognized on-line by fuzzy inferencing. The error vector is newly defined in a macroscopic elemental balance equation and utilized for the on-line recognition of the physiological state of the micro-organisms. Fuzzy membership functions are constructed from the error vectors and state recognition is performed successfully by fuzzy inferencing. In particular, a usual physiological state for yeast cultivation in which aerobic ethanol production is accompanied by very low cell growth can be recognized accurately.

On‐line state recognition in a yeast fed‐batch culture using error vectors

The physiological states with respect to cell growth and ethanol production in a yeast fed-batch culture expressed in linguistic form could be recognized on-line by fuzzy inferencing based on error vectors. The error vector was newly defined here in a macroscopic elemental balance equation. The physiological states for cell growth and ethanol production were characterized by error vectors using many experimental data from fed-batch cultures. Fuzzy membership functions were constructed from the frequency distributions of the error vectors and state recognition was performed by fuzzy inferencing. In particular, an unusual physiological state for a yeast cultivation, in which aerobic ethanol production was accompanied by very low cell growth, could be recognized accurately. According to the results of the state recognition, an energy parameter, the P/O ratio in the metabolic reaction model was adaptively estimated, and the cell growth was successfully evaluated with the estimated P/O. (c) 1995 John Wiley & Sons, Inc.

On-Line Physiological State Recognition and Parameter Estimation in the Metabolic Reaction Model Using Error Vectors

An on-line recognition system of the physiological states in a yeast fed-batch culture based on a fuzzy inferencing and a metabolic reaction model is discussed. State recognition of the yeast fermentation was performed by the fuzzy inferencing with error vectors. According to the results of the state recognition, P/O ratio in the metabolic reaction model was adaptively estimated. The amount of cell growth was estimated using the metabolic reaction model with the estimated P/O. The residual error by this method was compared with that by the least square parameter estimation.

159 Fuzzy control of ethanol concentration and its application to glutathione production in yeast fed-batch culture: H. Shimizu, K. Miura, C.G. Alfafara, S. Shioya, K. Suga, K. Suzuki, pp 433–436

159 Fuzzy control of ethanol concentration and its application to glutathione production in yeast fed-batch culture: H. Shimizu, K. Miura, C.G. Alfafara, S. Shioya, K. Suga, K. Suzuki, pp 433–436

Fuzzy control of ethanol concentration its application to maximum glutathione production in yeast fed‐batch culture

A fuzzy logic controller (FLC) for the control of ethanol concentration was developed and utilized to realize the maximum production of glutathione (GSH) in yeast fedbatch culture. A conventional fuzzy controller, which uses the control error and its rate of change in the premise part of the linguistic rules, worked well when the initial error of ethanol concentration was small. However, when the initial error was large, controller overreaction resulted in an overshoot.An improved fuzzy controller was obtained to avoid controller overreaction by diagnostic determination of "glucose emergency states" (i.e., glucose accumulation or deficiency), and then appropriate emergency control action was obtained by the use of weight coefficients and modification of linguistic rules to decrease the overreaction of the controller when the fermentation was in the emergency state. The improved fuzzy controller was able to control a constant ethanol concentration under conditions of large initial error.The improved fuzzy control system was used in the GSH production phase of the optimal operation to indirectly control the specific growth rate mu to its critical value micro(c). In the GSH production phase of the fed-batch culture, the optimal solution was to control micro to micro(c) in order to maintain a maximum specific GSH production rate. The value of micro(c) also coincided with the critical specific growth rate at which no ethanol formation occurs. Therefore, the control of micro to micro(c) could be done indirectly by maintaining a constant ethanol concentration, that is, zero net ethanol formation, through proper manipulation of the glucose feed rate. Maximum production of GSH was realized using the developed FLC; maximum production was a consequence of the substrate feeding strategy and cysteine addition, and the FLC was a simple way to realize the strategy.

Fuzzy Control of Ethanol Concentration and its Application to Glutathione Production in Yeast Fed-batch Culture

The aim of this article is to develop a fuzzy controller for ethanol concentration control in a yeast fed-batch culture. The proposed fuzzy controller can diagnose the state of fermentation and avoid the overfeeding and underfeeding of the substrate. Experimental evidence shows that controller performance is better as compared with PI and conventional fuzzy controllers. This fuzzy control system was applied to the optimal production of glutathione (GSH) in yeast fed-batch culture.

Optimal production of glutathione by controlling the specific growth rate of yeast in fed‐batch culture

The optimal of the specific growth rate was obtained with simple mathematical model in a yeast fed-batch cultures. The model was based on the mass balance around the fed-batch system and the relationship between the specific growth rate, mu, and the specific production rate of glutathione, rho(G). The optimal profile of mu was calculated as a bang-bang type, That is mu, should start from the maximum value, mu(max) and should be kept at mu(max); then mu should be switched to mu(c), which gives a maximum value of rho(G). It was proven from the maximum principle that switching was needed only once, with the switching time from mu(max) to mu(c) depending on the final required glutathione content. Finally, this ideal profile of mu for the maximum production of glutathione was realized by manipulating the substrates feed rate in the fed-batch culture. Using the extended Kalman filter and a programmed-controller/feedback-compensator (PF) system, mu could be controlled at the optimal profile obtained. As a result, the maximum production of glutathione was accomplished fairly successfully. However, further improvement in the controller performance for mu is desired. The control strategy employed here can be applied to other batch reaction processes.

Profile control of the specific growth rate in fed-batch experiments

The aim of this paper is to apply a computer control scheme to a laboratory scale fermentor so that the specific growth rate in a baker's yeast fed-batch culture, which cannot be measured directly, will follow as accurately as possible the desired profile specified in advance. Using an extended Kalman filter and programmed controller/feedback compensator (PF) system proposed previously, profile control of the specific growth rate (μ) was achieved experimentally in a baker's yeast fed-batch culture. Also, bang-bang type profile control of μ minimized the proportion of budding cells, which have a strong correlation with the fermentative activity in bread-making.

Simulation and Experimental Studies of the Profile Control of the Specific Growth Rate in a Fed-batch Culture

The aim of this paper is to develop and discuss a practically useful system so that the specific growth rate in a baker's yeast fed-batch cultute will follow the desired profile specified in advance as accurately as possible. For this, first, the specific growth rate should be estimated using indirect measurement of the overall growth rate or using online data of cell concentration. Based on the careful analysis of the previously proposed method, an improved method was proposed which uses moving averages and dynamic mass balancing. It was shown that the specific growth rate could be estimated with sufficient accuracy using an extended Kalman filtering algorithm using the estimated overall growth rate, or using the cell concentration. Secondly, the control scheme called "Prograrruned-controller/Feedback-compensator(PF) system " consists of a prograrruned controller and a feedback compensator has been used for the profile control of μ. The PF-MRAC in which the model reference adaptive controller was used for the compensator, was shown to be effective for this purpose. Finally, the profile control of the specific growth rate in a baker's yeast fed-batch culture was realized experimentally using the proposed control scheme. The experiments showed that the estimation and control schemes proposed were sufficiently useful.

Comparison of simple population models in a baker's yeast fed-batch culture

In this paper, three models describing the population balance of microorganisms based on the assumption of symmetric cell division are discussed in terms of whether it is possible or not to use them to explain the dynamic behavior of the RBC. The ideal optimal control laws of the overall specific growth rate which maximizes the fermentative activity in the making of bread are obtained based on three models, It is a very interesting feature that the optimal pattern of μ becomes a bang-bang-type one based on both the Age and Mass models. Fed-batch culture according to the ideal optimal control coincides with a synchronously dividing culture. From the experimental study, the age model is shown to be most suitable for explaining the data. The optimal control policy is, for the most part, achieved experimentally by manipulating the glucose feed rate in the fed-batch culture and is shown to be valid.

Optimal quality control of bakers' yeast fed‐batch culture using population dynamics

An optimal quality control policy for the overall specific growth rate of bakers' yeast, which maximizes the fermentative activity in the making of bread, was obtained by direct searching based on the mathematical model proposed previously. The mathematical model had described the age distribution of bakers' yeast which had an essential relationship to the ability of fermentation in the making of bread. The mathematical model is a simple aging model with two periods: Nonbudding and budding. Based on the result obtained by direct searching, the quality control of bakers' yeast fed-batch culture was performed and confirmed to be experimentally valid.