Effect of bioprocess conditions on growth and alkaline protease production by halotolerant Bacillus licheniformis BA17.

The effect of bioprocess conditions (pH and temperature) on the growth and alkaline protease production of halotolerant Bacillus licheniformis BA17 bioreactor cultures have been systematically analyzed using response surface methodology in order to assess the importance of these generally disregarded parameters. Two models were proposed differing by the choice of response variable. Under optimized bioprocess conditions, whole alkaline protease activity was about 3 fold higher than the activities obtained in the preliminary studies.

Model reduction and a priori kinetic parameter identifiability analysis using metabolome time series for metabolic reaction networks with linlog kinetics.

In this work, we present a time-scale analysis based model reduction and parameter identifiability analysis method for metabolic reaction networks. The method uses the information obtained from short term chemostat perturbation experiments. We approximate the time constant of each metabolite pool by their turn-over time and classify the pools accordingly into two groups: fast and slow pools.

A method for estimation of elasticities in metabolic networks using steady state and dynamic metabolomics data and linlog kinetics.

Background: Dynamic modeling of metabolic reaction networks under in vivo conditions is a crucial step in order to obtain a better understanding of the (dis)functioning of living cells. So far dynamic metabolic models generally have been based on mechanistic rate equations which often contain so many parameters that their identifiability from experimental data forms a serious problem. Recently, approximative rate equations, based on the linear logarithmic (linlog) format have been proposed as a suitable alternative with fewer parameters.