ABSTRACT
It was determined that Kx strains secrete an X factor which can inhibit all known Saccharomyces cerevisiae killer toxins (K1, K2, K28) and some toxins of other yeast species-the phenomenon not yet described in the scientific literature. It was shown that Kx type yeast strains posess a killer phenotype producing small but clear lysis zones not only on the sensitive strain α'1 but also on the lawn of S. cerevisiae K1, K2 and K28 type killer strains at temperatures between 20 and 30 °C. The pH at which killer/antikiller effect of Kx strain reaches its maximum is about 5.0-5.2. The Kx yeast were identified as to belong to S. cerevisiae species. Another newly identified S. cerevisiae killer strain N1 has killer activity but shows no antikilller properties against standard K1, K2 and K28 killer toxins. The genetic basis for Kx killer/antikiller phenotype was associated with the presence of M-dsRNA which is bigger than M-dsRNA of standard S. cerevisiae K1, K2, K28 type killer strains. Killer and antikiller features should be encoded by dsRNA. The phenomenon of antikiller (inhibition) properties was observed against some killer toxins of other yeast species. The molecular weight of newly identified killer toxins which produces Kx type strains might be about 45 kDa.
ABSTRACT
This paper has arisen as a result of teaching Models in Biology to undergraduates of Bioengineering at the Gediminas Technical University of Vilnius. The aim is to teach the students to use a fresh approach to the problems they are familiar with, to come up with an articulate verbal model after a mental effort, to express it in rigorous mathematical terms, to solve (with the aid of computers) corresponding equations, and finally, to analyze and interpret experimental data in terms of their (mathematical) models. Investigation of microbial growth provides excellent possibilities to combine laboratory exercises, mathematical modeling, and model-based data analysis. Application of mathematics in this field proved to be very fruitful in getting deeper insight into the processes of microbial growth. The step-by-step modeling resulted in an extended model of the growth covering conventional "lag," "exponential," and "stationary" phases. In contrast to known models (differential equations that can be solved only numerically), the present model is expressed symbolically as a finite combination of elementary functions. The approach can be applied in other areas of modern biology, such as dynamics of various cellular processes, enzyme and receptor kinetics, and others.
