Approach of white box mathematical modelling was used to develop dynamic mathematical model of glycerol cycle in baker's yeast Saccharomyces cerevisiae and by deterministic simulations explore an interaction between glycerol cycle and glycolysis. The key process in research was reaction of artificially expressed glycerol-2-dehydrogenase (Gld2) which catalyses glycerol transition into dihydroxyacetone and reduced nicotinamide adenine dinucleotide phosphate (NADPH). We put forward hypothesis that expression of Gld2 in S. cerevisiae could increase concentration of reduced cytosolic NADPH. Michaelis-Menten equation was used to describe a rate law of reactions of the model of glycerol cycle. Kinetic parameters Km (Michaelis - Menten constant) and Vmax (maximal velocity of reaction) were taken from BRENDA database and publications. To perform deterministic simulations of the model of glycerol cycle and glycolysis, an accomplished model of glycerol cycle was introduced into the mathematical model of glycolysis of Nielsen et al. (1998). Results suggested that Gld2 reaction could run even without glycolysis as long as glycerol and oxidized nicotinamide adenine dinucleotide phosphate (NADP+) was present in the system. Intracellular concentration of glycerol had a direct impact on formation and acumulation of dihydroxyacetone. Reduced nicotinamide adenine dinucleotide (NADH) concentration decreased significantly and rapidly when glycerol cycle was switched on suggesting that it could be a limiting factor of the system.

One of the biodiesel production problems is occurrence of a significant quantity (about 10%) of the by-product – glycerol. This problem is offered to solve by adaptation of bacteria Zymomonas mobilis, which is notable for ethanol production facilities. To be able to process glycerine into ethanol using Z.mobilis bacteria, the bacteria must be modified. At the same time, computer modelling analysis is required to assess specific modification affectivity in interconnection with other processes in bacteria. Computer model results of simulated experiment to understand and predict that the cells and biological processes are essential to reduce the number of experiments. This in turn reduces the necessary financial resources and time, bio-medical biotechnology, pharmaceutical and environmental problems. The model describes conversion of glycerol into bioethanol in Z.mobilis bacterial cell. First phase of model creation is creation of a structure model based on biochemical reactions using computer software CellDesigner. On the second phase of model creation, kinetic parameters which are available in literature were identified. Using the databases KEGG, SABIO-RK, BRENDA, reactants, kinetic parameters and reaction equation types were defined. Dynamic model of Z. mobilis biochemical network was created using computer software Copasi. The dynamic model describes conversion of glycerol into bioethanol in Z.mobilis bacterial cell. In this time simulation data of the computer model of natural organisms are not to confirm laboratory experimental data. Simulation data of the computer model are not correct to prevent this problem is required parameter estimation in computer software COPASI.

Computer models of bioprocesses have become an essential tool in biotechnology. This paper describes various bioprocess scaleup releated problems. The problems at different scales are explained. In this paper the use of computer model in E.coli fermentation scale-up from shake flask to laboratory scale bioreactor is discussed. A black box modelling approach was used. Fermentation results have been visualized and discussed. The computer model created in Matlab environment was used for bioprocess behaviour prediction. Possible bioprocess scale-up software improvements and bioprocess optimization are discussed.

The treatment of ketosis with glycerol was first introduced in the 1950s. Currently, the availability of glycerol as a feed supplement for dairy cattle has increased due to the rapid expansion of the biodiesel industry. It has been suggested that glycerol can be used as a dietary glucose precursor for dairy cows in a similar way to propylene glycol. This study examined the effect of orally administrated crude glycerol on milk production and composition, and energy-status related blood metabolites of primiparous (n=4) and multiparous (n=4) Holstein dairy cows in the first three weeks of lactation. The treatment group cows were given an oral drench of 500 mL of crude glycerol once a day before the morning feeding. Long-term oral drenching of crude glycerol had no effect on dry matter intake (DMI), but there was a positive effect on milk yield. Most milk composition values were not affected by the treatment, but treatment groups had lower milk protein levels. Blood glucose and insulin concentrations were declined with glycerol administration.

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.