Duran-Villalobos, CA;
Ogonah, O;
Melinek, B;
Bracewell, DG;
Hallam, T;
Lennox, B;
(2021)
Multivariate statistical data analysis of cell-free protein synthesis toward monitoring and control.
AIChE Journal
, Article e17257. 10.1002/aic.17257.
(In press).
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Abstract
The optimization and control of cell free protein synthesis (CFPS) presents an ongoing challenge due to the complex synergies and nonlinearities that cannot be fully explained in first principle models. This article explores the use of multivariate statistical tools for analyzing data sets collected from the CFPS of Cereulide monoclonal antibodies. During the collection of these data sets, several of the process parameters were modified to investigate their effect on the end‐point product (yield). Through the application of principal component analysis and partial least squares (PLS), important correlations in the process could be identified. For example, yield had a positive correlation with pH and NH3 and a negative correlation with CO2 and dissolved oxygen. It was also found that PLS was able to provide a long‐term prediction of product yield. The presented work illustrates that multivariate statistical techniques provide important insights that can help support the operation and control of CFPS processes.
| Type: | Article |
|---|---|
| Title: | Multivariate statistical data analysis of cell-free protein synthesis toward monitoring and control |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/aic.17257 |
| Publisher version: | https://doi.org/10.1002/aic.17257 |
| Language: | English |
| Additional information: | © 2021 The Authors. AIChE Journal published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Biopharmaceutical manufacturing, cell-free synthesis, process control, process data analytics, process optimization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10125845 |
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