Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-7682
Main Title: Accelerated Bioprocess Development of Endopolygalacturonase-Production with Saccharomyces cerevisiae Using Multivariate Prediction in a 48 Mini-Bioreactor Automated Platform
Author(s): Sawatzki, Annina
Hans, Sebastian
Narayanan, Harini
Haby, Benjamin
Krausch, Niels
Sokolov, Michael
Glauche, Florian
Riedel, Sebastian L.
Neubauer, Peter
Cruz Bournazou, Mariano Nicolas
Type: Article
Language Code: en
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.
URI: https://depositonce.tu-berlin.de//handle/11303/8548
http://dx.doi.org/10.14279/depositonce-7682
Issue Date: 21-Nov-2018
Date Available: 26-Nov-2018
DDC Class: 570 Biowissenschaften; Biologie
Subject(s): mini-bioreactors
high throughput bioprocess development
laboratory automation
biomanufacturing
digitalization
multivariate analysis
dynamical bioprocesses
Sponsor/Funder: DFG, TH 662/19-1, Open Access Publizieren 2017 - 2018 / Technische Universität Berlin
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Bioengineering
Publisher: MDPI
Publisher Place: Basel
Volume: 5
Issue: 4
Article Number: 101
Publisher DOI: 10.3390/bioengineering5040101
EISSN: 2306-5354
Appears in Collections:FG Bioverfahrenstechnik » Publications

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