Effect of Cell Morphology on Dead‐End Filtration of the Dimorphic Yeast Kluyveromycesmarxianus Var. marxianus NRRLy2415

McCarthy, A.A.; O’Shea, Daniel; Murray, Niamh T.; Walsh, Padraig K.; Foley, Greg

doi:10.1021/bp980006g

Cited by 47 publications

(25 citation statements)

References 18 publications

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“…In Fig. 2, the dependence of specific resistance on pressure is shown to be linear in agreement with other workers [6], [7] and [8] and there is a strong and complex dependence on pH of both the specific resistance at a given pressure and of the cake compressibility, where the latter is proportional to the slope of the α versus ΔP plot [7] and [34]. Fig.…”

Section: Filtration Behavioursupporting

confidence: 87%

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Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks

Mhurchú

Foley

2006

Journal of Membrane Science

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The specific cake resistance in dead-end filtration is a complex function of suspension properties and operating conditions. In this study, the specific resistance of resuspended dried bakers yeast suspensions was measured in a series of 150 experiments covering a range of pressures, cell concentrations, pHs, ionic strengths and membrane resistances. The specific resistance was found to increase linearly with pressure and exhibited a complex dependence on pH and ionic strength. The specific resistance data were correlated using an artificial neural network containing a single hidden layer with nine neurons employing the sigmoidal activation function. The network was trained with 104 training points, 13 validation points and 33 test points. Excellent agreement was obtained between the neural network and the test data with average errors of less than 10%. In addition, a network was trained for prediction of the filtrate flux directly from the system inputs and this approach is easily extended to crossflow filtration by adding inputs such as the crossflow velocity and channel height. An attempt was made to interpret the network weights for both the specific resistance and flux networks. The effective contribution of each input to the system output was computed in each case and showed trends that were as expected. Although network weights, and consequently the computed effect of each parameter, is different each time a network is changed (depending on the initial weights used in the training process), the variation was low enough for information contained in the network to be interpreted in a meaningful way.Keywords: Dead-end filtration; Specific cake resistance; Artificial neural network; Bakers yeast

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Section: Filtration Behavioursupporting

confidence: 87%

“…The equations used to correlate specific resistance data are varied but most authors use the classic power-law equation [3], [4] and [5] or, in some cases (especially for microbial suspensions), a linear relationship between specific resistance and pressure is employed [6], [7] and [8].…”

Section: Introductionmentioning

confidence: 99%

Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks

Mhurchú

Foley

2006

Journal of Membrane Science

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“…Nevertheless, under certain conditions, one particular morphology may dominate [11]. This wide morphology spectrum greatly affects fermentation performance, since it induces rheological changes and consequently leads to mass and heat transfer alterations in the bioreactor.…”

Section: Introductionmentioning

confidence: 99%

Morphological analysis of Yarrowia lipolytica under stress conditions through image processing

Kawasse

Amaral

Rocha-Leão

et al. 2003

Bioprocess and Biosystems Engineering

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Stress conditions (thermal and oxidative) were applied to Yarrowia lipolytica culture. A rearrangement in cell metabolism as well as dimorphism was observed under these conditions. An image analysis procedure was employed for morphology characterization, and a net increase of around 25% on hyphae formation was detected. A significant increment in total hyphal length was detected, compared with the control system. The results obtained lead to the consideration of a possible relationship between dimorphism and a cell response mechanism to stress conditions. Keywords Cell morphology, Yeast, Yarrowia lipolytica, Image analysis, Stress IntroductionYarrowia lipolytica is an aerobic micro-organism capable of producing important metabolites and has an intense secretory activity, which justifies efforts to use it in industry (as a biocatalyst), in molecular biology and in genetics studies. Dimorphism refers to the ability of fungi to grow in two distinct forms, usually as single oval cells or as a filament, and to be reversible between each one. The cell shape is controlled by environmental factors, as shown by some authors [1,2,3].Y. lipolytica has been considered an adequate model for dimorphism studies in yeasts, since it has an efficient system for genetic engineering transformation, and it is easy to distinguish between its morphological forms, in contrast to S. cerevisiae, which does not produce true filaments and exhibits pseudo-hyphae growth under nitrogen-limited conditions. The yeast-to-mycelium transition is associated with unipolar growth, asymmetric division, large polarly located vacuoles and repression of cell separation after division [4].Y. lipolytica has a hyphae diameter corresponding to 60-100% of its single cell stage [5,6]. The true mycelium consists of septated hyphae 3-5 m wide and several millimeters long. The apical cells frequently exceed 100 m, while the segments measure 50-70 m [7]. The germination exhibits a bipolar pattern, and there is no case of overposition in germination locus [6].It is believed that yeast dimorphism is related to a defense mechanism to adverse conditions, such as temperature and nutritional changes [8]. In wild strains, such as the one employed in this work, the hyphae growing ability can provide a selective advantage when facing conditions of stress [9,10]. It is known that, specifically in Y. lipolytica, the carbon source employed has no influence on cell morphology and has a medium pH. However, their morphology is considered to be strongly dependent on the nitrogen source and, if the mitochondrial respiration is inhibited, no hyphae formation is achieved [7,10].Fungal dimorphism is generally characterized by the presence of many intermediate morphological forms, displaying a broad distribution of cell sizes and shapes. Nevertheless, under certain conditions, one particular morphology may dominate [11]. This wide morphology spectrum greatly affects fermentation performance, since it induces rheological changes and consequently leads to mass and heat transfer...

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“…The difficulty in their case is not so much the size of the cells but the very different morphologies that can be observed. The possible interaction between the observed morphologies (A. awamori, [98,99] and niger [100] P. chrysogenum [101][102][103][104][105] S. fradiae [106,107] and tendae [108], basidiomycetes such as Schizophyllum commune [109], Fomes fomentarius [110] or Cyathus striatus [111] ), the mixing conditions and the viscous properties of the broth is one important question for improved process design, not only at the level of the fermenter but also for downstream processing steps such as filtration [112]. Product formation is related to morphology for many species: A. awamori [99,113], A. oryzae [114], A. niger, producer of itaconic and citric acids [115] and enzymes (polygalacturonidase or alpha-glucosidase).…”

Section: The Special Case Of Filamentous Speciesmentioning

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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Effect of Cell Morphology on Dead‐End Filtration of the Dimorphic Yeast Kluyveromycesmarxianus Var. marxianus NRRLy2415

Cited by 47 publications

References 18 publications

Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks

Dead-end filtration of yeast suspensions: Correlating specific resistance and flux data using artificial neural networks

Morphological analysis of Yarrowia lipolytica under stress conditions through image processing

Biomass Quantification by Image Analysis

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