Nonlinear Dynamics of Brewing Yeast Cell Growth in Alginate Micro-Beads

The nonlinear dynamics of brewing yeast cell growth in porous Ca-alginate matrices is considered experimentally and theoretically. The applications of alginate matrices include the reduction of internal mass transfer resistance, minimized cell leakage and growth restriction due to interactions between matrices and cell membranes comparatively to free cell culture conditions. The effects of micro-bead diameters in the range 0.3-2.0 mm on yeast cell growth were investigated. The stochastic mathematical model from the Langevin class is proposed for the interpretation of cell growth, affected by four micro-processes: micro-environmental quality changes due to nutrient diffusion into the micro-beads, cell leakage, repulsive interactions between boundary layers around the cells themselves, which contribute to the dynamics of cell growth as a negative, nonlinear feedback restriction and random kinetics effects. Such a model is used for the prediction of the optimal diameter of micro-beads, which ensures maximal final cell concentration. The results of cell growth in alginate matrices study have indicated an optimal diameter of 0.5-0.6 mm for micro-beads. Immobilized cells in these beads were not restricted significantly by mass transfer of nutrients and by cell leakage. The highest final cell concentration value indicated the largest feed-back restriction quantified by the constitutive parameter b.

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The rye Mutants Identify a Role for Ssn/Srb Proteins of the RNA Polymerase II Holoenzyme During Stationary Phase Entry in Saccharomyces cerevisiae

Genetics ◽

10.1093/genetics/157.1.17 ◽

2001 ◽

Vol 157 (1) ◽

pp. 17-26 ◽

Cited By ~ 3

Author(s):

Ya-Wen Chang ◽

Susie C Howard ◽

Yelena V Budovskaya ◽

Jasper Rine ◽

Paul K Herman

Keyword(s):

Saccharomyces Cerevisiae ◽

Cell Growth ◽

Rna Polymerase ◽

Stationary Phase ◽

Yeast Cell ◽

Rna Polymerase Ii ◽

Transcriptional Response ◽

Nutrient Deprivation ◽

Ras Signaling ◽

Rna Polymerase Ii Holoenzyme

Abstract Saccharomyces cerevisiae cells enter into a distinct resting state, known as stationary phase, in response to specific types of nutrient deprivation. We have identified a collection of mutants that exhibited a defective transcriptional response to nutrient limitation and failed to enter into a normal stationary phase. These rye mutants were isolated on the basis of defects in the regulation of YGP1 expression. In wild-type cells, YGP1 levels increased during the growth arrest caused by nutrient deprivation or inactivation of the Ras signaling pathway. In contrast, the levels of YGP1 and related genes were significantly elevated in the rye mutants during log phase growth. The rye defects were not specific to this YGP1 response as these mutants also exhibited multiple defects in stationary phase properties, including an inability to survive periods of prolonged starvation. These data indicated that the RYE genes might encode important regulators of yeast cell growth. Interestingly, three of the RYE genes encoded the Ssn/Srb proteins, Srb9p, Srb10p, and Srb11p, which are associated with the RNA polymerase II holoenzyme. Thus, the RNA polymerase II holoenzyme may be a target of the signaling pathways responsible for coordinating yeast cell growth with nutrient availability.

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COMPARATIVE STUDIES OF CELL GROWTH OF FRESHWATER MICROALGA CHLORELLA SP. IN PHOTOAUTOTROPHIC, HETEROTROPHIC AND MIXOTROPHIC CULTURES

Jurnal Teknologi ◽

10.11113/jt.v78.4349 ◽

2016 ◽

Vol 78 (7) ◽

Cited By ~ 1

Author(s):

Costantine Joannes ◽

Rachel Fran Mansa ◽

Suhaimi Md. Yasir ◽

Jedol Dayou

Keyword(s):

Cell Growth ◽

Cell Concentration ◽

Alternative Pathway ◽

Biodiesel Production ◽

Microalgae Cultivation ◽

Heterotrophic Culture ◽

Mixotrophic Culture ◽

Chlorella Sp ◽

Carbon Compounds ◽

Culture Mode

Lately, research on biodiesel production as a renewable and sustainable energy has become increasingly apparent due to the fact that fossil fuel is decreasing and the concern of global warming issues. The third generation of biofuel, which is microalgae-based biodiesel had gained interest over the last decade. The ability of microalgae to grow in various conditions is one of its advantages as the potential and promising feedstock for biodiesel. Microalgae can be cultivated in three modes such as photoautotrophic, heterotrophic and mixotrophic culture mode. Unlike photoautotrophic mode where light is required, the heterotrophic mode mainly utilized carbon compounds to grow. On the other hand, the mixotrophic mode is the condition where light and carbon compounds are supplied for microalgae culturing. This paper investigates the cell growth of Chlorella sp. cultivated in photoautotrophic, heterotrophic and mixotrophic culture mode. It was found that Chlorella sp. was capable of producing the highest cell concentration of 6.67 ± 0.56 x 106 cell mL-1 in the photoautotrophic mode for 23 days of cultivation period. This was 1.3 times and 3.2 times greater than the cell concentration in mixotrophic (5.02 ± 0.49 x 106 cell mL-1) and heterotrophic (2.03 ± 0.29 x 106 cell mL-1) culture, respectively. On the contrary, the highest specific growth rate obtained in the study was from heterotrophic mode (0.32 ± 0.04 day-1) followed by photoautotrophic and mixotrophic mode with 0.26 ± 0.05 day-1 and 0.20 ± 0.04 day-1, respectively. Chlorella sp. cell grew well under the photoautotrophic and mixotrophic mode. However, the insufficient of glucose level had contributed to lower cells productivity in the heterotrophic culture. Therefore, the mixotrophic mode could also be an alternative pathway in microalgae cultivation for biodiesel production if the glucose supplied was adequate and at the suitable level.

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Feeding of yeast cell wall extracts during a necrotic enteritis challenge enhances cell growth, survival and immune signaling in the jejunum of broiler chickens

Poultry Science ◽

10.1016/j.psj.2020.03.012 ◽

2020 ◽

Vol 99 (6) ◽

pp. 2955-2966

Author(s):

Casey N. Johnson ◽

Mohammed M. Hashim ◽

Christopher A. Bailey ◽

James A. Byrd ◽

Michael H. Kogut ◽

...

Keyword(s):

Cell Wall ◽

Cell Growth ◽

Yeast Cell ◽

Broiler Chickens ◽

Yeast Cell Wall ◽

Necrotic Enteritis ◽

Immune Signaling

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A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2002959117 ◽

2020 ◽

Vol 117 (31) ◽

pp. 18869-18879 ◽

Cited By ~ 2

Author(s):

Christopher Culley ◽

Supreeta Vijayakumar ◽

Guido Zampieri ◽

Claudio Angione

Keyword(s):

Neural Network ◽

Gene Expression ◽

Machine Learning ◽

Cell Growth ◽

Yeast Cell ◽

Metabolic Flux ◽

Data Augmentation ◽

Predictive Accuracy ◽

Expression Profiles ◽

Metabolic Reconstruction

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143Saccharomyces cerevisiaemutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

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Effects of the hydrodynamic environment and oxygen mass transfer on plant cell growth and milk-clotting protease production in a stirred-tank reactor

Engineering in Life Sciences ◽

10.1002/elsc.201100087 ◽

2012 ◽

Vol 12 (4) ◽

pp. 441-449 ◽

Cited By ~ 6

Author(s):

Sara Raposo ◽

Maria Emília Lima-Costa

Keyword(s):

Mass Transfer ◽

Cell Growth ◽

Plant Cell ◽

Stirred Tank ◽

Protease Production ◽

Oxygen Mass Transfer ◽

Tank Reactor ◽

Milk Clotting ◽

Milk Clotting Protease ◽

Hydrodynamic Environment

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INVESTIGATION OF GRAPHENE CHANNEL INTERACTION WITH YEAST CELL FOR CELL COUNTING APPLICATION

Jurnal Teknologi ◽

10.11113/jt.v78.9454 ◽

2016 ◽

Vol 78 (7-5) ◽

Author(s):

Fatin Norshafini Zainol ◽

Muhammad Syazwan Dollah ◽

Mohd Ridzuan Ahmad ◽

Shaharin Fadzli Abd Rahman

Keyword(s):

Yeast Cell ◽

Cell Concentration ◽

Cell Counting ◽

Suitable Material ◽

Channel Interaction ◽

Channel Resistance ◽

Cell Counter

Graphene superior and unique properties make it a suitable material for biosensor. In this work, graphene interaction with yeast cell is investigated for development of graphene-based cell counter. The fabricated graphene channel was characterized by means of two-terminal and solution-gated three-terminal measurement setup. The correlation between graphene channel resistance and cell concentration was confirmed. The yeast cell was found to give n-type doping which modulate the conductivity of graphene channel.

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