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Nonlinear state estimation as tool for online monitoring and adaptive feed in high-throughput cultivations
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  • Annina Kemmer,
  • Nico Fischer,
  • Terrance Wilms,
  • Linda Cai,
  • Sebastian Groß,
  • R. King,
  • Peter Neubauer,
  • Mariano Nicolas Cruz-Bournazou
Annina Kemmer
Technische Universität Berlin
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Nico Fischer
Technische Universität Berlin
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Terrance Wilms
Technische Universität Berlin
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Linda Cai
Technische Universität Berlin
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Sebastian Groß
Technische Universität Berlin
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R. King
Technische Universität Berlin
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Peter Neubauer
Technische Universität Berlin
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Mariano Nicolas Cruz-Bournazou
Technische Universität Berlin

Corresponding Author:mariano.n.cruzbournazou@tu-berlin.de

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Abstract

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.
23 Dec 2022Submitted to Biotechnology and Bioengineering
23 Dec 2022Submission Checks Completed
23 Dec 2022Assigned to Editor
23 Dec 2022Review(s) Completed, Editorial Evaluation Pending
23 Jan 2023Reviewer(s) Assigned
05 Mar 2023Editorial Decision: Revise Major
10 May 20231st Revision Received
10 May 2023Submission Checks Completed
10 May 2023Assigned to Editor
10 May 2023Review(s) Completed, Editorial Evaluation Pending
11 Jul 2023Editorial Decision: Accept