An all-in-one state-observer for protein refolding reactions using particle filters and delayed measurements

Jan Niklas Pauk, Chika Linda Igwe, Christoph Herwig, Julian Kager*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Proper monitoring as basis for process optimization and control of protein refolding reactions in real-time is difficult and currently available techniques are either expensive, not applicable in real-time or give only limited information about the ongoing process. Model-based methods such as particle filters (PFs) have been used in different biological systems for state-estimation to overcome difficulties arising from states that are hard or impossible to measure, often low measurement frequencies and high measurement delay. Since recent approaches had difficulties to overcome all these problems, a novel approach via a PF including a mechanistic model is used. The PF is calibrated and tuned with experimental data and its applicability validated with two additional experiments. It is shown how augmentation of model parameters can be used for state-estimation in real-time to better adapt to model inaccuracies, poor model calibration or application of the calibrated model to a new process. Furthermore, it is shown that the PF can deal with low measurement frequencies and high measurement delay, resulting in reliable tracking of the process with normalized root mean squared errors (NRMSE) of the native protein and folding intermediates between 3.44 and 6.62%, values in the range of 18 to 93% less compared to simple feed-forward simulation.
Original languageEnglish
Article number119774
JournalChemical Engineering Science
Volume287
Number of pages12
ISSN0009-2509
DOIs
Publication statusPublished - 2024

Keywords

  • Mechanistic model
  • Particle filter
  • Process monitoring
  • Protein refolding
  • State-Estimation

Fingerprint

Dive into the research topics of 'An all-in-one state-observer for protein refolding reactions using particle filters and delayed measurements'. Together they form a unique fingerprint.

Cite this