Development of a mathematical model of the continuous (chemostat) process of culturing pasteurella

One of the most important technological stages in the production of biological preparations intended for the specific prevention of infectious diseases is the cultivation of microorganisms. The synthesis of antigens occurs precisely at this technological stage of vaccine production; the effectiveness of immunopreparations depends on them. In the process of growing bacteria, it is necessary, simultaneously with an increase in the biomass yield, to ensure that the pathogen does not change its biological properties. To do this, it is necessary to create optimal conditions for cultivation, taking into account the physiological state of microorganisms. The technology of manufacturing bacterial vaccines is a multifaceted problem, the key direction of which is the development of controlled processes for the cultivation of microorganisms. At present, obtaining a bacterial mass of microorganisms for the manufacture of vaccines is based on a periodic method of cultivation, during which the properties of cells and the composition of the culture medium change unpredictably. According to a number of researchers, the most efficient in terms of accumulation of bacterial biomass is chemostat cultivation with limitation by the carbon source [1, 2]. The productivity of continuous (chemostat) cultivation of microorganisms significantly exceeds the productivity of the batch method. Therefore, very promising research aimed at organizing the processes of controlled cultivation and, in particular, on continuous methods of growing microorganisms, allowing you to create and maintain for a long time cultures with a constant and precisely defined biomass concentration, phase and growth rate, as well as the ratio of protective antigens [ 3, 4]. The aim of this work is to build an adequate mathematical model of the process of chemostat cultivation of Pasteurella in the production of anti-Pasteurella vaccine in order to optimize it. As a result of the research, the structure of the mathematical model of continuous cultivation of P. multocida was developed, its coefficients were determined, the adequacy of the model to the real process was verified, the obtained mathematical description of the process makes it possible to calculate and select the modes of chemostatic cultivation - the dilution rate D and the initial glucose concentration S0 - to obtain the optimal concentrations of viable pasteurella, the specified productivity values, the degree of substrate conversion, etc. in the manufacture of antibacterial vaccines. In addition, the obtained mathematical dependences make it possible to make a proposal on the metabolic mechanism for increasing the concentration of pasteurella at low dilution rates.

Genome-wide BigData analytics: Case of yeast stress signature detection

It has been generally recognized that BigData analytics presently have most significant impact on computer inference in life sciences, such as genome wide association studies (GWAS) in basic research and personalized medicine, and its importance will further increase in near future. In this work non-parametric separation of responsive yeast genes from experimental data obtained in chemostat cultivation under dilution rate and nutrient limitations with basic biogenic elements (C,N,S,P), and the specific leucine and uracil auxothropic limitations. Elastic net models are applied for the detection of the key responsive genes for each of the specific limitations. Bootstrap and perturbation methods are used to determine the most important responsive genes and corresponding quantiles applied to the complete data set for all of the nutritional and growth rate limitations. The model predicts that response of gene YOR348C, involved in proline metabolism, as the key signature of stress. Based on literature data, the obtained result are confirmed experimentally by the biochemistry of plants under physical and chemical stress, also by functional genomics of bakers yeast, and also its important function in human tumorogenesis is observed.