High‐throughput screening of optimal process conditions using model predictive control

Abstract

Modern biotechnological laboratories are equipped with advanced parallel mini‐bioreactor facilities that can perform sophisticated cultivation strategies (e.g., fed‐batch or continuous) and generate significant amounts of measurement data. These systems require not only optimal experimental designs that find the best conditions in very large design spaces, but also algorithms that manage to operate a large number of different cultivations in parallel within a well‐defined and tightly constrained operating regime. Existing advanced process control algorithms have to be tailored to tackle the specific issues of such facilities such as: a very complex biological system, constant changes in the metabolic activity and phenotypes, shifts of pH and/or temperature, and metabolic switches, to name a few. In this study we implement a model predictive control (MPC) framework to demonstrate: (1) the challenges in terms of mathematical model structure, state, and parameter estimation, and optimization under highly nonlinear and stiff dynamics in biological systems, (2) the adaptations required to enable the application of MPC in high throughput bioprocess development, and (3) the added value of MPC implementations when operating parallel mini‐bioreactors aiming to maximize the biomass concentration while coping with hard constrains on the dissolved oxygen tension profile.

Microbioreactors for nutrient‐controlled microbial cultures: Bridging the gap between bioprocess development and industrial use

“…[ 62 ] Here, chemostats were operated with a gradual change in dilution rate. The automated mini‐bioreactor platform developed by the TU Berlin combines robotic liquid controllers for complex feeding profiles [ 63 ] with advanced model predictive control, [ 64 ] thereby trading off between throughput and process control. The Robot Scientist by the group of Ross King is yet another example of high throughput automation where experimental space can be covered much more efficiently.…”

Thermal segment microwell plate control for automated liquid handling setups

“…[14][15][16][17][18] Academic literature has a few examples for the application of sensor networks and Smart Sensors in a biolab, which rely on a potpourri of systems, each equipped with sensors running in parallel forming a highly parallelized mesh of experiments. [19][20][21][22][23][24][25] In modern biolabs, sensors and devices are subjected to a diverse array of requirements. Requirements for sensor-based devices, as identified by various authors, include a wide range of features.…”

Nonlinear state estimation as tool for online monitoring and adaptive feed in high throughput cultivations

“…For example, Sinner et al (2022) reported an increase in the maximum growth rates for some substrates of a continuous process of Corynebacterium glutamicum growing on spent sulfite liquor, whereas Neubauer et al (2003) reported a decrease in glucose uptake after induction of recombinant protein production in an Escherichia coli fed‐batch process. Hence, adaptive approaches considering variations in model parameter changes during the bioprocess are needed to cope with the reduced description capabilities of tractable process models (Cruz Bournazou et al, 2016; Krausch et al, 2022; Sinner et al, 2022). For this reason, online monitoring of the biomass concentration is essential to have an accurate estimate of both, the current biomass concentration and the growth rate.…”

“…The added value of the tool is demonstrated in the redesign of the feed rate in the exponential feeding phase and after induction of recombinant protein production. Mears et al (2017) presented an overview on feed control strategies, including the popular generic model‐based control by feedback linearization (Abadli et al, 2021; Kager, Bartlechner, et al, 2022; Kager, Horst, et al, 2022) and model‐predictive control (Kawohl et al, 2007; Krausch et al, 2022; Waldraff et al, 1997). These allow the direct control of specific rates or the further definition of constraints.…”