The objective of the study was to examine how the circumstances of different bull daughters – Latvian Brown cow - group productivity levels correlate with the blood biochemical composition indices in Latvian conditions. The daughters of seven bulls, selected for our research trial, were kept in the same barn and under rearing conditions. Their blood samples were taken in a single sampling activity and analyzed for blood serum biochemical averages. Also Cu, Fe, Zn, Mn and Mg were detected in blood serum and the values found were compared with values fixed in regulatory documents. There were changes in Cu levels (daughter groups of four bulls), in Zn levels (one group); Mn level was lower in all groups and Mg level was higher in 6 groups. The altered biochemical parameters of blood were assessed from the pathogenetical point of view and compared with productivity averages in the progeny groups. The correlation factors suggested that there exist a strong positive correlation (p is less than 0.05) among a number of indicators, such as aspartate aminotransferase (ASAT) and protein r = 0.823; ASAT and somatic cell count (SCC), r = 0.737; blood protein and milk protein r = 0.903 - 0.917; albumins and protein r = 0.964; blood urea and milk protein r = 0.931 - 0.984 along with additional correlations. The correlation factors for the progeny group of each bull differed. The data obtained shows that the Latvian Brown cows crossed with other breeds produce cows with new genetic traits.

Metabolic analysis is one of the research focuses of systems biology. Two aspects of metabolic networks -network topology and stoichiometry - are what current researchers are most interested in, and both studies have revealed significant information. The research of the stoichiometric matrix of metabolic network has generated a series of powerful methodologies such as flux balance analysis (FBA). For FBA different methods execution are used different software like COBRA Toolbox and FBA-SimVis. The aim of this paper is to compare and analyze functionality of these two toolboxes, metabolic network data conformation conditions, and to compare all available FBA methods comparison in calculation possibilities and visual interpretation way. FBA, when analyzing all fluxes using different options, gives results in its metabolic network flow chart, although Cobra Toolbox returns the results in the matrix in number formats. FBA-SimVis for Steady state metabolic network models analysis is provided for a small metabolic network, because making some FBA analysis there are a ten possibilities to change an unlimited count of variables, to choose or change or optimize reactions as variables. Cobra Toolbox for Steady state metabolic network models analysis is provided for greater metabolic networks with hundreds or thousands of reactions. It allows changing an unlimited count manipulating and optimizing reactions fluxes.