Next Article in Journal
Laboratory Automation in Clinical Microbiology
Previous Article in Journal
Decellularized Human Umbilical Artery Used as Nerve Conduit
Article Menu
Issue 4 (December) cover image

Export Article

Open AccessArticle
Bioengineering 2018, 5(4), 101; https://doi.org/10.3390/bioengineering5040101

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.
Received: 16 October 2018 / Revised: 9 November 2018 / Accepted: 14 November 2018 / Published: 21 November 2018
Full-Text   |   PDF [4134 KB, uploaded 26 November 2018]   |  

Abstract

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
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

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.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Bioengineering EISSN 2306-5354 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top