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.
Robotic facilities that can perform advanced cultivations (e.g., fed‐batch or continuous) in high throughput have drastically increased the speed and reliability of the bioprocess development pipeline. 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 for the implementation of feedback control methods in miniaturized bioreactor systems, where 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. The filter exploits the information generated by the ammonia‐based pH control to enable the continuous estimation of the biomass concentration, a critical state to monitor the specific rates of production and consumption in the process. The objective in the selected case study is to ensure that the selected specific substrate consumption rate is tightly controlled throughout the complete Escherichia coli cultivations for recombinant production of an antibody fragment.
In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike Information Criterion (AIC). The best models were then used in a multi-model setup for robust product maximization. The experimental validation shows the highest product yield attained compared with the identification runs so far. In subsequent fermentations, the online measurements of CO 2 concentration, base consumption, and near-infrared spectroscopy (NIR) were used for model improvement. After model extension using expert knowledge, a single superior model could be identified. Model-based state estimation with a sigma-point Kalman filter (SPKF) was based on online measurement data, and this improved model enabled nonlinear real-time product maximization. The optimization increased the macrolactin D production even further by 28% compared with the initial robust multi-model offline optimization.
Increasing the comparability to large scale fermentations is a constant
aim during scale-down, including growth-limiting feeding of the carbon
source. Minibioreactor facilities greatly increase the throughput and
offer many advantages. However, online measurements in this small scale
often do not include critical process variables, necessary to control
the feed rate. State estimators utilize mathematical models to estimate
non-measurable states applying existing information about the inputs,
outputs, and measurement uncertainties. Though, existing applications
focus on bench-top or production-scale applications, where advanced
process analytical technologies such as spectroscopic methods are
available. Here, we present a concept for model-based nonlinear state
estimation in a high-throughput platform with 24 parallel experiments in
the mL-scale. An extended and a sigma-point Kalman filter are
implemented based on online measurements only. The dissolved oxygen
concentration and a relation of biomass growth and nitrogen consumption
are used as measurement input. The current state of the cultivation is
estimated iteratively throughout Escherichia coli cultivations
and used to maintain predefined growth setpoints by adapting the feed
rate. With this, we bring a further level of monitoring and process
understanding to minibioreactor systems, and therefore enable
automatization and optimization of miniaturized bioprocesses under
controlled cultivation conditions.
Pyrimidine and purine nucleoside phosphorylases catalyze the reversible phosphorolytic cleavage and formation of the glycosidic bond of purine and pyrimidine nucleosides, respectively, and are thus, key catalysts for the synthesis of new compounds. The selection of the best combination of enzyme and reaction conditions is not trivial, as each of the two enzymes can perform the reaction in two directions and thus, also competes with the other one for the reaction intermediates. A generic approach to the solution of this problem based on the formulation of a mixed integer dynamic optimization program using MOSAICmodeling is presented.
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