Cell culture process metabolomics together with multivariate data analysis tools opens new routes for bioprocess development and glycosylation prediction.

Multivariate latent variable methods have become a popular and versatile toolset to analyze bioprocess data in industry and academia. This work spans such applications from the evaluation of the role of the standard process variables and metabolites to the metabolomics level, that is, to the extensive number metabolic compounds detectable in the extracellular and intracellular domains. Given the substantial effort currently required for the measurement of the latter groups, a tailored methodology is presented that is capable of providing valuable process insights as well as predicting the glycosylation profile based on only four experiments measured over 12 cell culture days.

Outward electron transfer by Saccharomyces cerevisiae monitored with a bi-cathodic microbial fuel cell-type activity sensor.

A Janus head-like bi-cathodic microbial fuel cell was constructed to monitor the electron transfer from Saccharomyces cerevisiae to a woven carbon anode. The experiments were conducted during an ethanol cultivation of 170 g/l glucose in the presence and absence of yeast-peptone medium. First, using a basic fuel-cell type activity sensor, it was shown that yeast-peptone medium contains electroactive compounds.

Online monitoring of yeast cultivation using a fuel-cell-type activity sensor.

A microbial fuel-cell type activity sensor integrated into 500 mL and 3.2 L bioreactors was employed for ampero- (lA) and potentiometric (mV) measurements. The aim was to follow the microbial activity during ethanol production by Saccharomyces cerevisiae and to detect the end of carbohydrate consumption.