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 20 min and acetate, glucose, and OD 620 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.

A model‐based framework for parallel scale‐down fed‐batch cultivations in mini‐bioreactors for accelerated phenotyping

“…By this mechanism, the whole bioreactor block was aerated to 10 L air ·min −1 during cultivation. The detailed configuration of hardware‐software protocols, sampling algorithms, pH control routines, pulse feed scheduling and off‐line, at‐line and online analytics on the robot station during parallel cultivations were presented previously (Cruz Bournazou et al, ; Nickel, Cruz‐Bournazou, Wilms, Neubauer, & Knepper, ; Sawatzki et al, ).…”

Modelling concentration gradients in fed‐batch cultivations of E. coli – towards the flexible design of scale‐down experiments

“…The big benefit is that cellular reaction models which consider the response to oscillations can be developed and parametrized with much lower effort. Additionally, the run of such experiments in efficient parallel robotic experimental facilities would allow for rapid phenotyping of a large number of candidates under process relevant conditions in short times …”

“…Additionally, the run of such experiments in efficient parallel robotic experimental facilities would allow for rapid phenotyping of a large number of candidates under process relevant conditions in short times. [26][27][28][29]…”

Implementation of a Fully Automated Microbial Cultivation Platform for Strain and Process Screening

“…These fluctuations even offer the potential to improve scalability by modifying them to suit a specific target laboratory‐ or large‐scale reactor . This can be achieved by increasing or reducing feed pulse step time, also with the aid of substrate release systems when necessary to overcome robotic minimum step times …”