The production of dihydroxyacetone from glycerol employing aerobic cultures of Gluconobacter oxydans is studied. Dihydroxyacetone is one of the most important value-added products obtained from glycerol, a by-product of biodiesel production. The effect of organic nitrogen source and initial substrate concentrations has been studied together with the possibility of product inhibition. Afterward, the influence of the main operating conditions (temperature, shaking speed, and initial biomass concentration) on in vivo glycerol dehydrogenase activity has also been considered. The results show no evidence of glycerol inhibition, but an important product inhibition was detected, which has been taken into account in a kinetic model for enzymatic activity description. In terms of operating conditions, pH was found to exert a great impact on glycerol conversion, being necessary to keep it above 4 to ensure complete glycerol conversion. The minimum temperature that maximized enzymatic activity was found to be 30°C. In addition, a surprising decoupling between biomass concentration and dihydroxyacetone production rate was observed when adding increasing nitrogen source concentrations at a fixed shaking speed. Glycerol dehydrogenase activity remains constant despite the increase in biomass concentration, contrary to what would be expected. This fact revealed the existence of a rate limiting factor, identified subsequently as oxygen transfer rate depending on the biomass concentration.

In our previous work, a NAD(H)-dependent carbonyl reductase (GoCR) was identified from Gluconobacter oxydans, which showed moderate to high enantiospecificity for the reduction of different kinds of prochiral ketones. In the present study, the crystal structure of GoCR was determined at 1.65Å resolution, and a computational strategy concerning substrate-enzyme docking and all-atom molecular dynamics (MD) simulation was established to help understand the molecular basis of enantiopreference and enantiorecognition for GoCR, and to further guide the design and engineering of GoCR enantioselectivity. For the reduction of ethyl 2-oxo-4-phenylbutyrate (OPBE), three binding pocket residues, Cys93, Tyr149, and Trp193 were predicted to play a critical role in determining the enantioselectivity. Through site-directed mutagenesis, single-point mutant W193A was constructed and proved to reduce OPBE to ethyl (R)-2-hydroxy-4-phenylbutyrate (R-HPBE) with a significantly improved ee of >99% compared to 43.2% for the wild type (WT). Furthermore, double mutant C93V/Y149A was proved to even invert the enantioselectivity of GoCR to afford S-HPBE at 79.8% ee.

Systems Biology is a multi-disciplinary research field with the aim of understanding the function of complex processes in living organisms. These intracellular processes are described by biochemical networks. Experimental studies in alliance with computer simulation lead to a continually increasing amount of data in liaison with different layers of biochemical networks. Thus, visualization is very important for getting an overview of data in association with the network components. Omix is a software for the visualization of any data in biochemical networks. The unique feature of Omix is: the software is programmable by a scripting language called Omix Visualization Language (OVL). In Omix, the visualization of data coming from experiment or simulation is completely performed by the software user realized in concise OVL scripts. By this, visualization becomes most flexible and adaptable to the requirements of the user and can be adapted to new application fields. We present four case studies of visualizing data of diverse kind in biochemical networks on metabolic level by using Omix and the OVL scripting language. These worked examples demonstrate the power of OVL in conjunction with pleasing visualization, an important requirement for successful interdisciplinary communication in the interface between more experimental and more theoretical researchers.

The influence of the product inhibition by dihydroxyacetone (DHA) on Gluconobacter oxydans for a novel semi-continuous two-stage repeated-fed-batch process was examined quantitatively. It was shown that the culture was able to grow up to a DHA concentration of 80 kg m(-3) without any influence of product inhibition. The regeneration capability of the reversibly product inhibited culture from a laboratory-scale bioreactor system was observed up to a DHA concentration of about 160 kg m(-3). At higher DHA concentrations, the culture was irreversibly product inhibited. However, due to the robust membrane-bound glycerol dehydrogenase of G. oxydans, product formation was still active for a prolonged period of time. The reachable maximum final DHA concentration was as high as 220 kg m(-3). The lag phases for growth increased exponentially with increasing DHA threshold values of the first reactor stage. These results correlated well with fluorescence in situ hybridization (FISH) measurements confirming that the number of active cells decreased exponentially with increasing DHA concentrations.

This study investigated dextran synthesis from a commercial maltodextrin substrate using cell suspensions of G. oxydans NCIB 4943 as catalysts. Experiments were arranged according to a central composite statistical design. The effects of substrate concentration (10-100 g l-1), cell concentration (0.32-32.0 g wet weight l-1), time of reaction (8-48 h) and pH (3.5-5.5), each at three levels, on dextran yield and dextran molecular weight (MW), were investigated. Response surface methodology was used to assess factor interactions, and empirical models describing the two responses were fitted. Most of the variance in dextran yield could be explained by the fitted model (R2 = 0.96). Dextran yield ranged from 1.21 to 41.69%. The presence of significant negative quadratic effects of cell concentration and time indicated that dextran yield reached a plateau and thus, optimum levels of cell concentration and time could be identified to maximize dextran yield. Dextran MW ranged from 6.6 to 38 kDa and was characterized by the significant interactions of reaction time with substrate concentration and cell concentration. The model, however, could account for only 60% of the variance in dextran MW. Possible reasons for this are discussed.