Abstract
Nowadays control of the growth of Saccharomyces to obtain biomass or cellular wall components is crucial for specific industrial applications. The general aim of this contribution is to deal with experimental data obtained from yeast cells and from yeast cultures to attempt the integration of the two levels of information, individual and population, to progress in the control of yeast biotechnological processes by means of the overall analysis of this set of experimental data, and to assist in the improvement of an individual-based model, namely, INDISIM-Saccha. Populations of S. cerevisiae growing in liquid batch culture, in aerobic and microaerophilic conditions, were studied. A set of digital images was taken during the population growth, and a protocol for the treatment and analyses of the images obtained was established. The piecewise linear model of Buchanan was adjusted to the temporal evolutions of the yeast populations to determine the kinetic parameters and changes of growth phases. In parallel, for all the yeast cells analyzed, values of direct morphological parameters, such as area, perimeter, major diameter, minor diameter, and derived ones, such as circularity and elongation, were obtained. Graphical and numerical methods from descriptive statistics were applied to these data to characterize the growth phases and the budding state of the yeast cells in both experimental conditions, and inferential statistical methods were used to compare the diverse groups of data achieved. Oxidative metabolism of yeast in a medium with oxygen available and low initial sugar concentration can be taken into account in order to obtain a greater number of cells or larger cells. Morphological parameters were analyzed statistically to identify which were the most useful for the discrimination of the different states, according to budding and/or growth phase, in aerobic and microaerophilic conditions. The use of the experimental data for subsequent modeling work was then discussed and compared to simulation results generated with INDISIM-Saccha, which allowed us to advance in the development of this yeast model, and illustrated the utility of data at different levels of observation and the needs and logic behind the development of a microbial individual-based model.
Highlights
Saccharomyces cerevisiae, known as brewer’s yeast or bread yeast, is one of the yeasts with the greatest economic and social impact
The sets of boxplots presented are useful for assessing and comparing sample distributions. They display the temporal evolutions of 50% of central data with the location of means and medians of the samples for each time corresponding to direct and derived morphologic parameters studied for two of the eight replicates, one performed in aerobic conditions and the other in microaerophilic conditions
A collection of digital images of S. cerevisiae cells growing in two different initial concentrations of oxygen was processed to perform subsequently the statistical analysis of a set of morphologic parameters
Summary
Saccharomyces cerevisiae, known as brewer’s yeast or bread yeast, is one of the yeasts with the greatest economic and social impact. Saccharomyces cerevisiae is a facultative anaerobic yeast and a Crabtree-positive yeast. When the conditions of the environment vary S. cerevisiae must adapt to the environmental changes being forced to pass in a short period of time from aerobic conditions to microaerophilic and anaerobic conditions at the end, changing the type of metabolism depending on the concentration of oxygen present in its neighborhood. In order to obtain greater numbers of cells or larger cells with more cellular components usable in diverse industries, Saccharomyces must grow in a medium with oxygen available and low initial sugar concentration, to avoid the Crabtree effect. The yeast obtained is utilized as starter in fermented beverage industries, or as probiotic yeast with health benefit, and it is used to obtain cellular components such as proteins and polysaccharides (e.g., glucans), which are of great value as functional ingredients in the food industry (Arevalo-Villena et al, 2017)
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