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Mauss, P.; Drouin, J.F.; Pons, Marie‐Noëlle; Vivier, H.; Germain, Pierre; Louvel, Liliane; Vanhoutte, Bert

doi:10.1023/a:1018477308962

Cited by 10 publications

(1 citation statement)

References 8 publications

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“…Other vital stains take advantage of different cellular properties which can be correlated with cellular physiology: Propidium Iodide, Ethidium Bromide, Ethidium Monoazide, Calcofluor White have been widely used to indicate the presence of dead eukaryotes or prokaryotes cells. 2-(p-iodophenyl-)3)(p-nitrophenyl)-5-phenyl tetrazolium chloride (INT) belongs to a class of stains which can be used to determine if a cell or hyphal compartments [180] can maintain an internal reducing environment (Fig. 20a).…”

Section: ) Pellet Convex Area -Core Convex Areamentioning

confidence: 99%

Biomass Quantification by Image Analysis

Pons

Vivier

1999

Bioanalysis and Biosensors for Bioprocess Monitoring

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Microbiologists have always rely on microscopy to examine microorganisms. When microscopy, either optical or electron-based, is coupled to quantitative image analysis, the spectrum of potential applications is widened: counting, sizing, shape characterization, physiology assessment, analysis of visual texture, motility studies are now easily available for obtaining information on biomass. In this chapter the main tools used for cell visualization as well as the basic steps of image treatment are presented. General shape descriptors can be used to characterize the cell morphology, but special descriptors have been defined for filamentous microorganisms. Physiology assessment is often based on the use of fluorescent dyes. The quantitative analysis of visual texture is still limited in bioengineering but the characterization of the surface of microbial colonies may open new prospects, especially for cultures on solid substrates. In many occasions, the number of parameters extracted from images is so large that data-mining tools, such as Principal Components Analysis, are useful for summarizing the key pieces of information.

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Section: ) Pellet Convex Area -Core Convex Areamentioning

confidence: 99%

Biomass Quantification by Image Analysis

Pons

Vivier

1999

Bioanalysis and Biosensors for Bioprocess Monitoring

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Modeling and measurements of fungal growth and morphology in submerged fermentations

Cui

Okkerse

Lans

et al. 1998

Biotechnol. Bioeng.

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Generalizing results from fungal fermentations is difficult due to their high sensitivity toward slight variation in starting conditions, poor reproducibility, and difference in strains. In this study a mathematical model is presented in which oxygen transfer, agitation intensity, dissolved oxygen tension, pellet size, formation of mycelia, the fraction of mycelia in the total biomass, carbohydrate source consumption, and biomass growth are taken into account. Two parameters were estimated from simulation, whereas all others are based on measurements or were taken from literature. Experimental data are obtained from the fermentations in both 2 L and 100 L fermentors at various conditions. Comparison of the simulation with experiments shows that the model can fairly well describe the time course of fungal growth (such as biomass and carbohydrate source concentrations) and fungal morphology (such as pellet size and the fraction of pellets in the total biomass). The model predicts that a stronger agitation intensity leads to a smaller pellet size and a lower fraction of pellets in the total biomass. At the same agitation intensity, pellet size is hardly affected by the dissolved oxygen tension, whereas the fraction of mycelia decreases slightly with an increase of the dissolved oxygen tension in the bulk. All of these are in line with observations at the corresponding conditions. © 1998 John Wiley & Sons, Inc. Biotechnol Bioeng 60: 216–229, 1998.

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A model for cellulase production from Trichoderma reesei in an airlift reactor

Bannari

Vermette³

et al. 2012

Biotech & Bioengineering

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A mathematical model for cellulase production by Trichoderma reesei RUT-C30 grown in a cellulose medium with lactose as fed batch in an airlift reactor is proposed. To describe adequately the mass transfer between the air bubbles and the broth, it uses computational fluid dynamics (CFD) including multiphase Eulerian-Eulerian formulation, with a detailed description of the bubble size distribution through the use of the population balance model (PBM) and the class method (CM). The kinetics of the biomass growth is further coupled to the fluid flow conditions using partial differential equations for all the species involved, providing detailed information of important reactor conditions such as the distribution of oxygen, cellulose, and the shear stress within the reactor over the entire period of fermentation. Predicted results agree well with the available overall measurements for a typical fed-batch operation and detailed profiles of the predicted concentration fields are discussed from an engineering viewpoint.

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Untitled

Cited by 10 publications

References 8 publications

Biomass Quantification by Image Analysis

Biomass Quantification by Image Analysis

Modeling and measurements of fungal growth and morphology in submerged fermentations

A model for cellulase production from Trichoderma reesei in an airlift reactor

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