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

Niels Krausch,Benjamin Haby,Mariano Cruz Bournazou,Peter Neubauer,Harini Narayanan,Florian Glauche,Sebastian Riedel,Michael Sokolov,Annina Sawatzki,Sebastian Hans

doi:10.3390/bioengineering5040101

Niels Krausch, Benjamin Haby + Show 8 more

Open Access

https://doi.org/10.3390/bioengineering5040101

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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.

Highlights

In biomanufacturing, processes developed in R&D often suffer setbacks during transfer to industrial production [1]
Since high throughput (HT) systems focus on increase of the number of parallel experiments, a trade-off must be met sacrificing the sophistication of cultivations’ monitoring and controls and its relevance for industrial scale
16 experimental conditions were carried out in triplicates in 48 MBRs to evaluate the influence of substrate availability and feeding strategy on recombinant protein production in S. cerevisiae

Summary

Introduction

Processes developed in R&D often suffer setbacks during transfer to industrial production [1]. In comparison to lab-scale bioreactors, MBRs allow for a higher experimental throughput—e.g., the fast screening of large strain libraries [5] or a great number of experimental conditions [6,7]—while still enabling the implementation of large-scale process conditions such as feeding, closed loop controls and techniques for scale-down experiments [8,9]. Their integration into liquid handling robots allows for execution of multiple manipulations in parallel. Due to the high number of parallel experiments, multiple experimental set-ups can be tested including replicates, which increases the reliability and transferability of the generated data for scale-up purposes

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Conclusion