Literature DB >> 20811925

Analysis of the effect of inoculum characteristics on the first stages of a growing yeast population in beer fermentations by means of an individual-based model.

M Ginovart1, C Prats, X Portell, M Silbert.   

Abstract

The yeast Saccharomyces cerevisiae has a limited replicative lifespan. The cell mass at division is partitioned unequally between a larger, old parent cell and a smaller, new daughter cell. Industrial beer fermentations maintain and reuse yeast. At the end of fermentation a portion of the yeast is 'cropped' from the vessel for 'serial repitching'. Harvesting yeast may select a population with an imbalance of young and aged individuals, but the output of any bioprocess is dependent on the physiology of each single cell in the population. Unlike continuous models, individual-based modelling is an approach that considers each microbe as an individual, a unique and discrete entity, with characteristics that change throughout its life. The aim of this contribution is to explore, by means of individual-based simulations, the effects of inoculum size and cell genealogical age on the dynamics of virtual yeast fermentation, focussing on: (1) the first stages of population growth, (2) the mean biomass evolution of the population, (3) the rate of glucose uptake and ethanol production, and (4) the biomass and genealogical age distributions. The ultimate goal is to integrate these results in order to make progress in the understanding of the composition of yeast populations and their temporal evolution in beer fermentations. Simulation results show that there is a clear influence of these initial features of the inocula on the subsequent growth dynamics. By contrasting both the individual and global properties of yeast cells and populations, we gain insight into the interrelation between these two types of data, which helps us to deal with the macroscopic behaviour observed in experimental research.

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Year:  2010        PMID: 20811925     DOI: 10.1007/s10295-010-0840-4

Source DB:  PubMed          Journal:  J Ind Microbiol Biotechnol        ISSN: 1367-5435            Impact factor:   3.346


  18 in total

Review 1.  Replicative ageing and senescence in Saccharomyces cerevisiae and the impact on brewing fermentations.

Authors:  Christopher D Powell; Sylvie M Van Zandycke; David E Quain; Katherine A Smart
Journal:  Microbiology       Date:  2000-05       Impact factor: 2.777

2.  INDISIM, an individual-based discrete simulation model to study bacterial cultures.

Authors:  Marta Ginovart; Daniel López; Joaquim Valls
Journal:  J Theor Biol       Date:  2002-01-21       Impact factor: 2.691

3.  Exploring the lag phase and growth initiation of a yeast culture by means of an individual-based model.

Authors:  Marta Ginovart; Clara Prats; Xavier Portell; Moises Silbert
Journal:  Food Microbiol       Date:  2010-05-11       Impact factor: 5.516

4.  Individual-based modelling of bacterial cultures to study the microscopic causes of the lag phase.

Authors:  Clara Prats; Daniel López; Antoni Giró; Jordi Ferrer; Joaquim Valls
Journal:  J Theor Biol       Date:  2006-03-09       Impact factor: 2.691

Review 5.  Mathematical modelling methodologies in predictive food microbiology: a SWOT analysis.

Authors:  Jordi Ferrer; Clara Prats; Daniel López; Josep Vives-Rego
Journal:  Int J Food Microbiol       Date:  2009-01-24       Impact factor: 5.277

6.  Individual-based modelling: an essential tool for microbiology.

Authors:  Jordi Ferrer; Clara Prats; Daniel López
Journal:  J Biol Phys       Date:  2008-07-19       Impact factor: 1.365

Review 7.  Flow cytometry and cell sorting of heterogeneous microbial populations: the importance of single-cell analyses.

Authors:  H M Davey; D B Kell
Journal:  Microbiol Rev       Date:  1996-12

8.  The impact of brewing yeast cell age on fermentation performance, attenuation and flocculation.

Authors:  Chris D Powell; David E Quain; Katherine A Smart
Journal:  FEMS Yeast Res       Date:  2003-04       Impact factor: 2.796

9.  Towards understanding of the complex structure of growing yeast populations.

Authors:  Chiara Cipollina; Marina Vai; Danilo Porro; Christos Hatzis
Journal:  J Biotechnol       Date:  2006-10-26       Impact factor: 3.307

Review 10.  Analysis and modeling of growing budding yeast populations at the single cell level.

Authors:  Danilo Porro; Marina Vai; Marco Vanoni; Lilia Alberghina; Christos Hatzis
Journal:  Cytometry A       Date:  2009-02       Impact factor: 4.355

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  1 in total

1.  Digital Image Analysis of Yeast Single Cells Growing in Two Different Oxygen Concentrations to Analyze the Population Growth and to Assist Individual-Based Modeling.

Authors:  Marta Ginovart; Rosa Carbó; Mónica Blanco; Xavier Portell
Journal:  Front Microbiol       Date:  2018-01-04       Impact factor: 5.640

  1 in total

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