High yield production of a soluble bifidobacterial β-galactosidase (BbgIV) inE. coliDH5α with improved catalytic efficiency for the synthesis of prebiotic galactooligosaccharides

The bifidobacterial β-galactosidase (BbgIV) was produced in E. coli DH5α at 37 and 30 °C in a 5 L bioreactor under varied conditions of dissolved oxygen (dO2) and pH. The yield of soluble BbgIV was significantly (P < 0.05) increased once the dO2 dropped to 0–2% and remained at such low values during the exponential phase. Limited dO2 significantly (P < 0.05) increased the plasmid copy number and decreased the cells growth rate. Consequently, the BbgIV yield increased to its maximum (71–75 mg per g dry cell weight), which represented 20–25% of the total soluble proteins in the cells. In addition, the specific activity and catalytic efficiency of BbgIV were significantly (P < 0.05) enhanced under limited dO2 conditions. This was concomitant with a change in the enzyme secondary structure, suggesting a link between the enzyme structure and function. The knowledge generated from this work is very important for producing BbgIV as a biocatalyst for the development of a cost-effective process for the synthesis of prebiotic galactooligosaccharides from lactose.

Efficient non-linear data assimilation in geophysical fluid dynamics

New ways of combining observations with numerical models are discussed in which the size of the state space can be very large, and the model can be highly nonlinear. Also the observations of the system can be related to the model variables in highly nonlinear ways, making this data-assimilation (or inverse) problem highly nonlinear. First we discuss the connection between data assimilation and inverse problems, including regularization. We explore the choice of proposal density in a Particle Filter and show how the ’curse of dimensionality’ might be beaten. In the standard Particle Filter ensembles of model runs are propagated forward in time until observations are encountered, rendering it a pure Monte-Carlo method. In large-dimensional systems this is very inefficient and very large numbers of model runs are needed to solve the data-assimilation problem realistically. In our approach we steer all model runs towards the observations resulting in a much more efficient method. By further ’ensuring almost equal weight’ we avoid performing model runs that are useless in the end. Results are shown for the 40 and 1000 dimensional Lorenz 1995 model.