Online bioprocess data generation, analysis, and optimization for parallel fed-batch fermentations in milliliter scale.

Abstract

Bioprocess development, optimization, and control in mini-bioreactor systems require information about essential process parameters, high data densities, and the ability to dynamically change process conditions. We present an integration approach combining a parallel mini-bioreactor system integrated into a liquid handling station (LHS) with a second LHS for offline analytics. Non-invasive sensors measure pH and DO online. Offline samples are collected every 20min and acetate, glucose, and OD620 subsequently analyzed offline. All data are automatically collected, analyzed, formalized, and used for process control and optimization. Fed-batch conditions are realized via a slow enzymatic glucose release system. The integration approach was successfully used to apply an online experimental re-design method to eight Escherichia coli fed-batch cultivations. The method utilizes generated data to select the following experimental actions online in order to reach the optimization goal of estimating E. coli fed-batch model parameters with as high accuracy as possible. Optimal experimental designs were re-calculated online based on the experimental data and implemented by introducing pulses via the LHS to the running fermentations. The LHS control allows for various implementations of advanced control and optimization strategies in milliliter scale.