Modelling of the substrate heterogeneities experienced by a limited microbial population in scale-down and in large-scale bioreactors

Delvigne, Frank; Lejeune, Annick; Destain, Jacqueline; Thonart, Philippe

doi:10.1016/j.cej.2006.03.034

Cited by 23 publications

(18 citation statements)

References 13 publications

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“…It is clear that these values are far from reality, but they allow us to develop a good idea of the hydrodynamic conditions in the reactors. These considerations have been discussed by Delvigne et al [42].…”

Section: Hydrodynamic Modelling Of Scale-down Reactorsmentioning

confidence: 95%

Influence of bioreactor hydraulic characteristics on a Saccharomyces cerevisiae fed-batch culture: hydrodynamic modelling and scale-down investigations

Lejeune¹,

Delvigne²,

Thonart³

2009

J Ind Microbiol Biotechnol

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Yeast is a widely used microorganism at the industrial level because of its biomass and metabolite production capabilities. However, due to its sensitivity to the glucose effect, problems occur during scale-up to the industrial scale. Hydrodynamic conditions are not ideal in large-scale bioreactors, and glucose concentration gradients can arise when these bioreactors are operating in fed-batch mode. We have studied the effects of such gradients in a scale-down reactor, which consists of a mixed part linked to a non-mixed part by a recirculation pump, in order to mimic the hydrodynamic conditions encountered at the large scale. During the fermentation tests in the scale-down reactor, there was a drop in both biomass yield (ratio between the biomass produced and the glucose added) and trehalose production and an increase in both fermentation time (time between inoculation and beginning of stationary phase) and ethanol production. We have developed a stochastic model which explains these effects as the result of an induction process determined mainly by the hydrodynamic conditions. The concentration profiles experienced by the microorganisms during the scale-down tests were expressed and linked to the biomass yields of the scale-down tests.

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Section: Hydrodynamic Modelling Of Scale-down Reactorsmentioning

confidence: 95%

Influence of bioreactor hydraulic characteristics on a Saccharomyces cerevisiae fed-batch culture: hydrodynamic modelling and scale-down investigations

Lejeune¹,

Delvigne²,

Thonart³

2009

J Ind Microbiol Biotechnol

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“…The most critical parameter that has to be taken into account in order to simulate efficiently glucose gradients generated in the SDR is the pulse frequency of the feed pump. In general, different categories of behavior can be depicted in function of the relative importance of the feed pump frequency and the mixing time of the SDR or the residence time inside the recycle loop 25. For a time interval between two pulses ( T pulse ) smaller than the global mixing time of the system, the mean concentrations experienced by the microorganisms are very low.…”

Section: Methodsmentioning

confidence: 99%

Green fluorescent protein (GFP) leakage from microbial biosensors provides useful information for the evaluation of the scale‐down effect

Delvigne¹,

Brognaux²,

Francis³

et al. 2011

Biotechnology Journal

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Mixing deficiencies can be potentially detected by the use of a dedicated whole cell microbial biosensor. In this work, a csiE promoter induced under carbon-limited conditions was involved in the elaboration of such biosensor. The cisE biosensor exhibited interesting response after up and down-shift of the dilution rate in chemostat mode. Glucose limitation was accompanied by green fluorescent protein (GFP) leakage to the extracellular medium. In order to test the responsiveness of microbial biosensors to substrate fluctuations in large-scale, a scale-down reactor (SDR) experiment was performed. The glucose fluctuations were characterized at the single cell level and tend to decrease the induction of GFP. Simulations run on the basis of a stochastic hydrodynamic model have shown the variability and the frequencies at which biosensors are exposed to glucose gradient in the SDR. GFP leakage was observed to a great extent in the case of a culture operated in well-mixed fed-batch mode, by comparison with those operated in SDR. GFP leakage seems to be correlated to a higher membrane permeability, confirming previous studies highlighting a better cell viability in cultures operated in a fluctuating environment. Our results suggest that GFP leakage could be used in parallel to the normal GFP biosensor function in order to assess microbial viability in process conditions.

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“…Simplified approaches have been proposed, i.e. a deterministic-stochastic compartment model allowing the simulation of individual microenvironmental profiles experienced by 30 000 microbial cells [34][35][36]. This approach could greatly reduce the computational cost and save resources for the integration of complex biological mechanisms.…”

Section: Implication Of Microbial Heterogeneity In Bioprocessesmentioning

confidence: 99%

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

Delvigne

Goffin

2013

Biotechnology Journal

127

102

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Heterogeneity or segregation of microbial populations has been the subject of much research, but the real impact of this phenomenon on bioprocesses remains poorly understood. The main reason for this lack of knowledge is the difficulty in monitoring microbial population heterogeneity under dynamic process conditions. The main concepts resulting in microbial population heterogeneity in the context of bioprocesses have been summarized by two distinct hypotheses. The first involves the individual history of microbial cells or the "path" followed during their residence time inside the process equipment. The second hypothesis involves a coordinated response by the microbial population as a bet-hedging strategy, in order to cope with process-related stresses. The respective contribution of each hypothesis to microbial heterogeneity in bioprocesses is still unclear. This illustrates the fact that, although microbial phenotypic heterogeneity has been thoroughly investigated at a fundamental level, the implications of this phenomenon in the context of microbial bioprocesses are still subject to debate. At this time, automated flow cytometry is the best technique for investigating microbial heterogeneity under process conditions. However, dedicated software and relevant biomarkers are needed for the proper integration of flow cytometry as a bioprocess control tool.

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Modelling of the substrate heterogeneities experienced by a limited microbial population in scale-down and in large-scale bioreactors

Cited by 23 publications

References 13 publications

Influence of bioreactor hydraulic characteristics on a Saccharomyces cerevisiae fed-batch culture: hydrodynamic modelling and scale-down investigations

Influence of bioreactor hydraulic characteristics on a Saccharomyces cerevisiae fed-batch culture: hydrodynamic modelling and scale-down investigations

Green fluorescent protein (GFP) leakage from microbial biosensors provides useful information for the evaluation of the scale‐down effect

Microbial heterogeneity affects bioprocess robustness: Dynamic single‐cell analysis contributes to understanding of microbial populations

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