Next Article in Journal
Laboratory Automation in Clinical Microbiology
Previous Article in Journal
Decellularized Human Umbilical Artery Used as Nerve Conduit
Open AccessArticle

Accelerated Bioprocess Development of Endopolygalacturonase-Production with Saccharomyces cerevisiae Using Multivariate Prediction in a 48 Mini-Bioreactor Automated Platform

1
Department of Bioprocess Engineering, Department of Biotechnology, Technische Universität Berlin, Ackerstr. 71-76, ACK24, D-13355 Berlin, Germany
2
ETH Zürich, Rämistrasse 101, CH-8092 Zurich, Switzerland
3
DataHow AG, c/o ETH Zürich, HCl, F137, Vladimir-Prelog-Weg 1, CH-8093 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Bioengineering 2018, 5(4), 101; https://doi.org/10.3390/bioengineering5040101
Received: 16 October 2018 / Revised: 9 November 2018 / Accepted: 14 November 2018 / Published: 21 November 2018
Mini-bioreactor systems enabling automatized operation of numerous parallel cultivations are a promising alternative to accelerate and optimize bioprocess development allowing for sophisticated cultivation experiments in high throughput. These include fed-batch and continuous cultivations with multiple options of process control and sample analysis which deliver valuable screening tools for industrial production. However, the model-based methods needed to operate these robotic facilities efficiently considering the complexity of biological processes are missing. We present an automated experiment facility that integrates online data handling, visualization and treatment using multivariate analysis approaches to design and operate dynamical experimental campaigns in up to 48 mini-bioreactors (8–12 mL) in parallel. In this study, the characterization of Saccharomyces cerevisiae AH22 secreting recombinant endopolygalacturonase is performed, running and comparing 16 experimental conditions in triplicate. Data-driven multivariate methods were developed to allow for fast, automated decision making as well as online predictive data analysis regarding endopolygalacturonase production. Using dynamic process information, a cultivation with abnormal behavior could be detected by principal component analysis as well as two clusters of similarly behaving cultivations, later classified according to the feeding rate. By decision tree analysis, cultivation conditions leading to an optimal recombinant product formation could be identified automatically. The developed method is easily adaptable to different strains and cultivation strategies, and suitable for automatized process development reducing the experimental times and costs. View Full-Text
Keywords: mini-bioreactors; high throughput bioprocess development; laboratory automation; biomanufacturing; digitalization; multivariate analysis; dynamical bioprocesses mini-bioreactors; high throughput bioprocess development; laboratory automation; biomanufacturing; digitalization; multivariate analysis; dynamical bioprocesses
Show Figures

Graphical abstract

MDPI and ACS Style

Sawatzki, A.; Hans, S.; Narayanan, H.; Haby, B.; Krausch, N.; Sokolov, M.; Glauche, F.; Riedel, S.L.; Neubauer, P.; Cruz Bournazou, M.N. Accelerated Bioprocess Development of Endopolygalacturonase-Production with Saccharomyces cerevisiae Using Multivariate Prediction in a 48 Mini-Bioreactor Automated Platform. Bioengineering 2018, 5, 101.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop