Development of a kinetic model for the alcoholic fermentation of must

Model-driven insights into the effects of temperature on metabolism

Temperature affects cellular processes at different spatiotemporal scales, and identifying the genetic and molecular mechanisms underlying temperature responses paves the way to develop approaches for mitigating the effects of future climate scenarios. A systems view of the effects of temperature on cellular physiology can be obtained by focusing on metabolism since: (i) its functions depend on transcription and translation and (ii) its outcomes support organisms' development, growth, and reproduction. Here we provide a systematic review of modelling efforts directed at investigating temperature effects on properties of single biochemical reactions, system-level traits, metabolic subsystems, and whole-cell metabolism across different prokaryotes and eukaryotes. We compare and contrast computational approaches and theories that facilitate modelling of temperature effects on key properties of enzymes and their consideration in constraint-based as well as kinetic models of metabolism. In addition, we provide a summary of insights from computational approaches, facilitating integration of omics data from temperature-modulated experiments with models of metabolic networks, and review the resulting biotechnological applications. Lastly, we provide a perspective on how different types of metabolic modelling can profit from developments in machine learning and models of different cellular layers to improve model-driven insights into the effects of temperature relevant for biotechnological applications.

Modelling the effects of assimilable nitrogen addition on fermentation in oenological conditions

Alcoholic fermentation in oenological conditions is a biological process carried out under significant physiological constraints: deficiency of nitrogen and other nutriments (vitamins, lipids …) and different stresses (pH and osmotic). In literature, few models have been proposed to describe oenological fermentations. They focused on the initial conditions and did not integrate nitrogen addition during the fermentation process which is a widespread practice. In this work, two dynamic models of oenological fermentation are proposed to predict the effects of nitrogen addition at two different timings: at the beginning of the process and during the fermentation experiment. They were validated and compared against existing models showing an accurate fit to experimental data for CO₂ release and CO₂ production rate.

Consideration of Maintenance in Wine Fermentation Modeling

We show that a simple model with a maintenance term can satisfactorily reproduce the simulations of several existing models of wine fermentation from the literature, as well as experimental data. The maintenance describes a consumption of the nitrogen that is not entirely converted into biomass. We show also that considering a maintenance term in the model is equivalent to writing a model with a variable yield that can be estimated from data.

Mild Pretreatments to Increase Fructose Consumption in Saccharomyces cerevisiae Wine Yeast Strains

The present research investigates the effect of different pretreatments on glucose and fructose consumption and ethanol production by four Saccharomyces cerevisiae wine strains, three isolated and identified from different wine regions in Turkey and one reference strain. A mild stress temperature (45 °C, 1 h) and the presence of ethanol (14% v/v) were selected as pretreatments applied to cell cultures prior to the fermentation step in synthetic must. The goodness fit of the mathematical models was estimated: linear, exponential decay function and sigmoidal model were evaluated with the model parameters R² (regression coefficient), RMSE (root mean square error), MBE (mean bias error) and χ² (reduced Chi-square). Sigmoidal function was determined as the most suitable model with the highest R² and lower RMSE values. Temperature pretreatment allowed for an increase in fructose consumption rate by two strains, evidenced by a t90 value 10% lower than the control. One of the indigenous strains showed particular promise for mild temperature treatment (45 °C, 1 h) prior to the fermentation step to reduce residual glucose and fructose in wine. The described procedure may be effective for indigenous yeasts in preventing undesirable sweetness in wines.

Saccharomyces cerevisiae and S. kudriavzevii Synthetic Wine Fermentation Performance Dissected by Predictive Modeling

Wineries face unprecedented challenges due to new market demands and climate change effects on wine quality. New yeast starters including non-conventional Saccharomyces species, such as S. kudriavzevii, may contribute to deal with some of these challenges. The design of new fermentations using non-conventional yeasts requires an improved understanding of the physiology and metabolism of these cells. Dynamic modeling brings the potential of exploring the most relevant mechanisms and designing optimal processes more systematically. In this work we explore mechanisms by means of a model selection, reduction and cross-validation pipeline which enables to dissect the most relevant fermentation features for the species under consideration, Saccharomyces cerevisiae T73 and Saccharomyces kudriavzevii CR85. The pipeline involved the comparison of a collection of models which incorporate several alternative mechanisms with emphasis on the inhibitory effects due to temperature and ethanol. We focused on defining a minimal model with the minimum number of parameters, to maximize the identifiability and the quality of cross-validation. The selected model was then used to highlight differences in behavior between species. The analysis of model parameters would indicate that the specific growth rate and the transport of hexoses at initial times are higher for S. cervisiae T73 while S. kudriavzevii CR85 diverts more flux for glycerol production and cellular maintenance. As a result, the fermentations with S. kudriavzevii CR85 are typically slower; produce less ethanol but higher glycerol. Finally, we also explored optimal initial inoculation and process temperature to find the best compromise between final product characteristics and fermentation duration. Results reveal that the production of glycerol is distinctive in S. kudriavzevii CR85, it was not possible to achieve the same production of glycerol with S. cervisiae T73 in any of the conditions tested. This result brings the idea that the optimal design of mixed cultures may have an enormous potential for the improvement of final wine quality.

Valorization of exhausted sugar beet cossettes by successive hydrolysis and two fermentations for the production of bio-products

Exhausted sugar beet cossettes (ESBC) show an enormous potential as a source of sugars for the production of bio-products. Enzyme hydrolysis with the combined effect of mainly cellulases, xylanases and pectinases, turned out to be very efficient, obtaining almost double the concentration of sugars measured with the sole action of Celluclast® and β-glucosidase, and increasing 5 times the hydrolysis rate. As the sole pretreatment, ESBC soaked in the hydrolysis buffer were autoclaved, avoiding the application of severe conventional biomass pretreatments. Moreover, a promising alternative for the complete utilization of glucose, xylose, arabinose, mannose and maltose contained in ESBC is proposed in this paper. It consists of sequential fermentation of sugars released in the hydrolysis step to produce bioethanol and lactic acid as main bio-products. Compared to separate fermentations, with this strategy glucose and hemicellulose derived sugars were completely consumed and the 44% of pectin derived sugars.

New approaches to the kinetic study of alcoholic fermentation by chromatographic techniques

The kinetics of the fermentation process has gained increasing interest, not only in the scientific community, but in the industrial world as well. Information concerning the improvement of batch fermentation performance may potentially be valuable for the designing of scale-up processes. Intensive studies have been conducted with the use of various chromatographic techniques, such as conventional gas chromatography, reversed-flow gas chromatography (RFGC), high-performance liquid chromatography, field-flow fractionation and others. In the present study, specific focus is placed on the employment of RFGC, a method that can successfully be applied for the determination of physicochemical quantities, such as reaction rate constants and activation energies, at each phase of the alcoholic fermentation. In contrast to conventional chromatographic techniques, RFGC can lead to substantial information referring to the evaluation of fermentation kinetics at any time of the process. Moreover, gravitational field-flow fractionation, a sub-technique of field-flow fractionation, presents the ability to monitor the proliferation of Saccharomyces cerevisiae cells through their elution profiles that can be related to the different cell growth stages. The combination of the two techniques can provide important information for kinetic study and the distinction of the growth phases of yeast cell proliferation during alcoholic fermentations conducted under different environmental conditions.

The study of the influence of temperature and initial glucose concentration on the fermentation process in the presence of Saccharomyces cerevisiae yeast strain immobilized on starch gels by reversed-flow gas chromatography

The technique of reversed-flow gas chromatography (RFGC) was employed for the determination of the alcoholic fermentation phases and of kinetic parameters for free and immobilized cell systems, at different initial glucose concentrations and temperature values. In addition to this, due to its considerable advantages over other techniques, RFGC was used for the characterization of a new biocatalyst, yeast cells immobilized on starch gel, and especially wheat starch gel. Immobilization of wine yeast Saccharomyces cerevisiae AXAZ-1 was accomplished on wheat and corn starch gels in order to prepare new biocatalysts with great interest for the fermentation industry. The RFGC led with great accuracy, resulting from a literature review, to the determination of reaction rate constants and activation energies at each phase of the fermentation processes. A maximum value of rate constants was observed at initial glucose concentration of 205 g/L, where a higher number of yeast cells was observed. The increase of glucose concentrations had a negative influence on the growth of AXAZ-1 cells and rate constants were decreased. The decrease of fermentation temperature caused a substantial reduction in the viability of immobilized cells as well as in rate constant values. Activation energies of corn starch gel presented lower values than those of wheat starch gel. However, the two supports showed higher catalytic efficiency than free cell systems, proving that starch gels may act as a promoter of the catalytic activity of the yeast cells involved in the fermentation process.

Temperature-dependent kinetic model for nitrogen-limited wine fermentations

A physical and mathematical model for wine fermentation kinetics was adapted to include the influence of temperature, perhaps the most critical factor influencing fermentation kinetics. The model was based on flask-scale white wine fermentations at different temperatures (11 to 35 degrees C) and different initial concentrations of sugar (265 to 300 g/liter) and nitrogen (70 to 350 mg N/liter). The results show that fermentation temperature and inadequate levels of nitrogen will cause stuck or sluggish fermentations. Model parameters representing cell growth rate, sugar utilization rate, and the inactivation rate of cells in the presence of ethanol are highly temperature dependent. All other variables (yield coefficient of cell mass to utilized nitrogen, yield coefficient of ethanol to utilized sugar, Monod constant for nitrogen-limited growth, and Michaelis-Menten-type constant for sugar transport) were determined to vary insignificantly with temperature. The resulting mathematical model accurately predicts the observed wine fermentation kinetics with respect to different temperatures and different initial conditions, including data from fermentations not used for model development. This is the first wine fermentation model that accurately predicts a transition from sluggish to normal to stuck fermentations as temperature increases from 11 to 35 degrees C. Furthermore, this comprehensive model provides insight into combined effects of time, temperature, and ethanol concentration on yeast (Saccharomyces cerevisiae) activity and physiology.

Fermentation kinetics including product and substrate inhibitions plus biomass death: a mathematical analysis

We propose an analytical solution of the kinetic equations describing fermentations. Equations are solved in phase space, i.e. the biomass concentration is written explicitly as a function of the substrate concentration. These results hold even when cell death and an arbitrary number of substrate/product inhibitions are accounted for. Moreover, constant yield needs not be assumed.

Kinetic model for nitrogen-limited wine fermentations

A physical and mathematical model for wine fermentation kinetics has been developed to predict sugar utilization curves based on experimental data from wine fermentations with various initial nitrogen and sugar concentrations in the juice. The model is based on: (1) yeast cell growth limited by nitrogen; (2) sugar utilization rates and ethanol production rates proportional solely to the number of viable cells; and (3) a death rate for cells proportional to alcohol content. All but one parameter in the model can be estimated from existing data. However, experiments to find this final parameter, a constant describing cell death, indicate that cell death may not be the critical factor in determining fermentation kinetics as cell viability remains significant until sugar utilization has ceased. The model, nevertheless, predicts a transition from normal to sluggish to stuck fermentations as initial nitrogen levels decrease. It also predicts that fermentations with high initial Brix levels may go to completion when supplemented with nitrogen in the form of ammonia. Therefore, we hypothesize that the model is valid but that ethanol causes the yeast cells to become inactive while remaining viable. Experimental verification of the model has been performed using flask-scale experiments. The model has also been used to evaluate the possibility of using nitrogen or viable cell additions to avoid or correct problem (i.e., sluggish or stuck) fermentations.

Using historical data for bioprocess optimization: modeling wine characteristics using artificial neural networks and archived process information

Optimization of fermentation processes is a difficult task that relies on an understanding of the complex effects of processing inputs on productivity and quality outputs. Because of the complexity of these biological systems, traditional optimization methods utilizing mathematical models and statistically designed experiments are less effective, especially on a production scale. At the same time, information is being collected on a regular basis during the course of normal manufacturing and process development that is rarely fully utilized. We are developing an optimization method in which historical process data is used to train an artificial neural network for correlation of processing inputs and outputs. Subsequently, an optimization routine is used in conjunction with the trained neural network to find optimal processing conditions given the desired product characteristics and any constraints on inputs. Wine processing is being used as a case study for this work. Using data from wine produced in our pilot winery over the past 3 years, we have demonstrated that trained neural networks can be used successfully to predict the yeast-fermentation kinetics, as well as chemical and sensory properties of the finished wine, based solely on the properties of the grapes and the intended processing. To accomplish this, a hybrid neural network training method, Stop Training with Validation (STV), has been developed to find the most desirable neural network architecture and training level. As industrial historical data will not be evenly spaced over the entire possible search space, we have also investigated the ability of the trained neural networks to interpolate and extrapolate with data not used during training. Because a company will utilize its own existing process data for this method, the result of this work will be a general fermentation optimization method that can be applied to fermentation processes to improve quality and productivity.