Robotic facilities that can perform advanced cultivation (e.g., fed-batch or continuous) in high throughput have drastically increased the speed and reliability of the bioproduct development pipeline in the last decades. Still, developing reliable analytical technologies, that can cope with the throughput of the cultivation system, has proven to be very challenging. On the one hand, the analytical accuracy suffers from the low sampling volumes, and on the other hand, the number of samples that must be treated rapidly is very large. These issues have been a major limitation to implement feedback control methods in miniaturized bioreactor systems, where the observations of the process states are typically obtained after the experiment has finished. In this work, we implement a Sigma-Point Kalman filter in a high-throughput platform with 24 parallel experiments at the mL-scale to demonstrate its viability and added value in high throughput experiments. This method exploits the information generated by the ammonia-based pH control to enable the continuous estimation of biomass, a critical state to monitor the specific rates of production and consumption in the process. The objective in our case study is to ensure that the selected specific growth rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of antibody fragment.

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, e.g. by product induction, to name a few. In this work we implement a model-predictive control (MPC) approach based framework to demonstrate: 1) the challenges in terms of mathematical model structure, state and parameter estimation, and optimization under highly nonlinear and stiff constraints in biological systems, 2) the adaptations required to enable its application in High Throughput Bioprocess Development (HTBD), 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.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, e.g., by induction of product formation, to name a few.In this work 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 (HTBD), 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.