Physiology of Saccharomyces cerevisiae during cell cycle oscillations

Duboc, Ph.; Marison, I. W.; Stockar, Urs von

doi:10.1016/0168-1656(96)01566-0

Cited by 56 publications

(54 citation statements)

References 30 publications

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“…Jones and Kompala (1999) applied their deterministic model to study the effects of changes in dilution rate and the oxygen mass transfer coefficient on oscillatory behavior. These two variables can change significantly the occurrence and the nature of oscillations (Beuse et al, 1998;Duboc et al, 1996;Sweere et al, 1988) and are commonly used as manipulated variables for reactor control, with the dilution rate being preferred (Dochain and Perrier, 1997;Henson and Seborg, 1992).…”

Section: The Lyapunov Exponentmentioning

confidence: 99%

“…Most modeling efforts have thus focused either in detail on the cellular metabolism with little attention to extracellular transport or primarily on the macroscopic variables of interest through mass balances coupled to heavily lumped kinetics. Both kinds of models have been reviewed recently (Duboc et al, 1996;Patnaik, 2003) and are therefore not discussed here.These models were based on observations with small, laboratory-scale reactors, where nonideal features such as process noise and spatial gradients can usually be ignored. However, these effects become significant in the realistic conditions of production-scale bioreactors, thus making it difficult to translate laboratory-scale models directly to larger bioreactors (Gillard and Tragardh, 1999;Rohner and Meyer, 1995).…”

mentioning

confidence: 98%

“…Such information is even more scanty for oscillating fermentations. However, in view of the prevalence and importance of oscillating microbial cultures, and the difficulties of formulating adequate mathematical models for realistic conditions (Duboc et al, 1996;Murray et al, 2001;Patnaik, 2003), it is purposeful to analyze the effect of noise on deterministic oscillations. Because of the availability of biochemical and physiological studies, its industrial importance and many observations of oscillatory behavior, S. cerevisiae is a good organism for such analyses.…”

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confidence: 99%

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Lyapunov comparison of noise‐filtering methods for oscillating yeast cultures

Patnaik

2004

AIChE Journal

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Under certain conditions, continuous cultures of the budding yeast Saccharomyces cerevisiae exhibit steady oscillations with time in some key concentrations such as those of the cell mass, carbon substrate (glucose), product (ethanol), storage carbohydrate, and dissolved oxygen. These oscillations have been reported in small, laboratory-scale reactors (Bellgardt, 1994;Beuse et al., 1998;Murray et al., 2001;Satroutdinov et al., 1992), which are largely unaffected by environmental disturbances. The nature of the oscillations, including the absence of oscillatory behavior itself, depends on the operating conditions, notably the dilution rate and the mass transfer rate of oxygen to the fermentation broth (Beuse et al., 1998;Jones and Kompala, 1999).Both metabolic processes and transport between the cells and the extracellular fluid contribute to the occurrence and control of oscillations, and their interactions are complex and not yet fully understood (Patnaik, 2003). This limitation makes it difficult to propose quantitative descriptions of oscillatory fermentations that are adequate without being too complicated. Most modeling efforts have thus focused either in detail on the cellular metabolism with little attention to extracellular transport or primarily on the macroscopic variables of interest through mass balances coupled to heavily lumped kinetics. Both kinds of models have been reviewed recently (Duboc et al., 1996;Patnaik, 2003) and are therefore not discussed here.These models were based on observations with small, laboratory-scale reactors, where nonideal features such as process noise and spatial gradients can usually be ignored. However, these effects become significant in the realistic conditions of production-scale bioreactors, thus making it difficult to translate laboratory-scale models directly to larger bioreactors (Gillard and Tragardh, 1999;Rohner and Meyer, 1995). To facilitate this scale-up and be able to replicate the high performances achievable in the laboratory, the control of disturbances is one important aspect of bioreactor optimization.Process noise complicates measurements and control, and can seriously affect bioreactor performance. Published industrial data (Glassey et al., 1994;Montague and Morris, 1994;Rohner and Meyer, 1995) illustrate the extent of fluctuations possible. Analyses of ethanol production by S. cerevisiae (Sweere et al., 1988) and Zymomonas mobilis (Patnaik, 1994) under nonoscillatory conditions show that noise carried by a feed stream can undermine productivity and also drive a fermentation to chaotic behavior. Because fed-batch and continuous fermentations are practiced in many applications, inflow noise is of serious concern on an industrial scale.Despite the recognized importance of noise in bioreactor operation, there is limited information on transitions from monotonic to chaotic behavior and on restoration of the original noise-free performance. Such information is even more scanty for oscillating fermentations. However, in view of the prevalence and importan...

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Section: The Lyapunov Exponentmentioning

confidence: 99%

mentioning

confidence: 98%

mentioning

confidence: 99%

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Lyapunov comparison of noise‐filtering methods for oscillating yeast cultures

Patnaik

2004

AIChE Journal

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“…As previously reported for other strains of S. cerevisiae, the CBS 8066 population exhibited spontaneous oscillations during aerobic cultivation. This phenomenon was presumably due to spontaneous cell synchronization (11,29). The oscillations eventually damped out, and no measurements were obtained until a completely nonoscillatory steady state had been reached.…”

Section: Methodsmentioning

confidence: 99%

Effects of Furfural on the Respiratory Metabolism of Saccharomyces cerevisiae in Glucose-Limited Chemostats

Horváth

Franzén

Taherzadeh

et al. 2003

Appl Environ Microbiol

148

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Effects of furfural on the aerobic metabolism of the yeast Saccharomyces cerevisiae were studied by performing chemostat experiments, and the kinetics of furfural conversion was analyzed by performing dynamic experiments. Furfural, an important inhibitor present in lignocellulosic hydrolysates, was shown to have an inhibitory effect on yeast cells growing respiratively which was much greater than the inhibitory effect previously observed for anaerobically growing yeast cells. The residual furfural concentration in the bioreactor was close to zero at all steady states obtained, and it was found that furfural was exclusively converted to furoic acid during respiratory growth. A metabolic flux analysis showed that furfural affected fluxes involved in energy metabolism. There was a 50% increase in the specific respiratory activity at the highest steady-state furfural conversion rate. Higher furfural conversion rates, obtained during pulse additions of furfural, resulted in respirofermentative metabolism, a decrease in the biomass yield, and formation of furfuryl alcohol in addition to furoic acid. Under anaerobic conditions, reduction of furfural partially replaced glycerol formation as a way to regenerate NAD ؉ . At concentrations above the inlet concentration of furfural, which resulted in complete replacement of glycerol formation by furfuryl alcohol production, washout occurred. Similarly, when the maximum rate of oxidative conversion of furfural to furoic acid was exceeded aerobically, washout occurred. Thus, during both aerobic growth and anaerobic growth, the ability to tolerate furfural appears to be directly coupled to the ability to convert furfural to less inhibitory compounds.An important problem in fermentative conversion of lignocellulose to ethanol is the severe inhibitory effects often exerted by lignocellulosic hydrolysates (17,21). Furfural is a characteristic compound present in dilute acid hydrolysates, particularly in hydrolysates from deciduous woods, in which the furfural concentration can be about 2 to 3 g/liter (37). Furfural has been found to severely inhibit metabolism in the yeast Saccharomyces cerevisiae under anaerobic conditions both during batch cultivation (5,7,27) and in glucose-limited chemostats (9,12,35). Furfural has been reported to have inhibitory effects on the specific growth rate, as well as on the fermentation rate of yeasts (4, 28). However, only few studies have described the effects of furfural under aerobic conditions. These effects are important, since inhibition caused by furfural during respiratory growth has a great impact on yeast propagation in an ethanol production plant based on a lignocellulosic feedstock. Taherzadeh et al. (39) compared the levels of conversion of furfural in anaerobic and aerobic batch cultures of S. cerevisiae growing on glucose. It was found that furfural was mainly converted to furfuryl alcohol by exponentially growing cells under both conditions and that the specific conversion rate was 0.6 g/g ⅐ h. Almost the same value for the maximum...

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“…Unstructured modelling approaches, based on the Monod equation and its descendants with the assumption of balanced growth and product formation 27 , are often reproached with the fact that they inadequately describe the response of a microorganism population to environmental disturbances. According to Sweere et al 28 available structured models can be distinguished in different types from approaches based on a structured biochemical pathway 8,26,29 to more segregated modelling approaches regarding different cell ages 2,11 . These approaches consider balanced growth, or are specified to describe particular biochemical pathways inside structured models and therefore represent the basis for the modelling approach introduced in this work.…”

Section: -2863(9'8-32mentioning

confidence: 99%

A Model Based Simulation of Brewing Yeast Propagation

Kurz

Mieleitner

Becker

et al. 2002

Journal of the Institute of Brewing

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A comprehensive kinetic model of yeast propagation in breweries is introduced. It represents the basis for a control strategy aimed at the provision of optimal inoculum at the starting time of subsequent industrial beer fermentations. In the metabolic modelling approach presented, respiratory metabolism on sugars and ethanol and fermentative metabolism respectively, are included. Occurring limitations, due to specific nutritional data of the growth medium, were taken into account for sugar, nitrogen, ethanol and oxygen. Correspondingly, inhibitions of the metabolism by ethanol and high sugar concentrations were formulated. The model especially represents the Crabtree-effect. For model validation, literature data were used and selected experiments within the relevant range of manipulated variables (temperature, dissolved oxygen) were conducted. Model based simulations matched these data with a deviation of 5.6% and with standard deviations of 5.6% after an adaptation of three variable parameters. A relationship between the temperature and the variable parameters could be extracted, which allowed predictive simulations of the yeast propagation process using non-isothermal trajectories. It was shown, that with a precise adjustment of trajectories of both, temperature and dissolved oxygen, the crop time of the inoculum could be varied within a period of 7 to 50 h to maintain high fermentation activity for the subsequent anaerobic fermentation.

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Physiology of Saccharomyces cerevisiae during cell cycle oscillations

Cited by 56 publications

References 30 publications

Lyapunov comparison of noise‐filtering methods for oscillating yeast cultures

Lyapunov comparison of noise‐filtering methods for oscillating yeast cultures

Effects of Furfural on the Respiratory Metabolism of Saccharomyces cerevisiae in Glucose-Limited Chemostats

A Model Based Simulation of Brewing Yeast Propagation

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