In his study the kinetics of the ammonium transport to he hydrocarbon-oxidizing yeast cells and the specific features of its regulation as one of the aspects of catabolite nitrogen repression were investigated. As the objects 3 mesophilic strains of C. maltosu, the thermotolerant strain C. rugosa as well as a collection of the thermotolerant strain C. blunkii BKM-Y-462 type were selected.One of the major objects of industrial biotechnological processes aimed a t producing single-cell protein is the yeast of the genus Candida. The practical value of the products obtained as feedstock for cattle and food for human beings is primarily determined by the protein content in the biomass and its aminoacid composition. The protein content in a product is known to depend on genetic properties of the strain used as well as on the conditions of its cultivation and postfermentation treatment of the biomass. I n spite of the fact that the yeast of the genus Candida has been widely used for a long period of time for obtaining single-cell protein from different kinds of raw materials, the nitrogen metabolism of the yeast involved has been studied insufficiently which prevents the regulation required for the nitrogen metabolism under industrial conditions. The aim of this work has been to study the kinetics of the ammonium transport to the hydrocarbon-oxidizing yeast cells and the specific features of its regulation as one of the aspects of catabolite nitrogen repression. As the objects of this study (Tab. 1) highly productive strains of hydrocarbon oxidizing yeast were selected namely 3 mesophilic strains of C. maltosa, which differ from each other by the level of protein content in the biomass, and the thermotolerant strain C. rugosa, as well as a collection of the thermotolerant strain C. blankii BKM-Y-462 type, which does not oxidize hydrocarbons. Thermotolerant strains are characterized by having the protein content in the biomass a t the level of low-protein mesophilic strains. Previous investigations studying a change of such parameters as the rate of dilution, growth temperature and pH value of the medium have shown that the optimal value of parameters for yeast growth also provides the highest protein content in biomass. When optimal parameters are chosen, the protein content changes to a lesser extent than other nitrogen-containing components of the cell. 5 Acta Biotechnol. 10 (1990) 2

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The diversity and the stability of the microbial community are associated with microecological interactions between its members. Antagonism is one type of interaction, which particularly determines the benefits that probiotics bring to host health by suppressing opportunistic pathogens and microbial contaminants in food. Mathematical models allow for quantitatively predicting intrapopulation relationships. The aim of this study was to create predictive models for bacterial contamination outcomes depending on the probiotic antagonism and prebiotic concentration. This should allow an improvement in the screening of synbiotic composition for preventing gut microbial infections. The functional model (fermentation) was based on a three-stage continuous system, and the distal colon section (N2, pH 6.8, flow rate 0.04 h) was simulated. The strains Bifidobacterium adolescentis ATCC 15703 and Bacillus cereus ATCC 9634 were chosen as the model probiotic and pathogen. Oligofructose Orafti P95 (OF) was used as the prebiotic at concentrations of 2, 5, 7, 10, 12, and 15 g/L of the medium. In the first stage, the system was inoculated with Bifidobacterium, and a dynamic equilibrium (Bifidobacterium count, lactic, and acetic acids) was achieved. Then, the system was contaminated with a 3-day Bacillus suspension (spores). The microbial count, as well as the concentration of acids and residual carbohydrates, was measured. A Bacillus monoculture was studied as a control. The stationary count of Bacillus in monoculture was markedly higher. An increase (up to 8 h) in the lag phase was observed for higher prebiotic concentrations. The specific growth rate in the exponential phase varied at different OF concentrations. Thus, the OF concentration influenced two key events of bacterial infection, which together determine when the maximal pathogen count will be reached. The mathematical models were developed, and their accuracies were acceptable for Bifidobacterium (relative errors ranging from 1.00% to 2.58%) and Bacillus (relative errors ranging from 0.74% to 2.78%) count prediction.

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