Model-assisted DoE software: optimization of growth and biocatalysis in Saccharomyces cerevisiae bioprocesses

André Moser,Kim B Kuchemüller,Sahar Deppe,Ralf Pörtner,Volker C Hass,Tanja Hernández Rodríguez,Björn Frahm,Johannes Möller

doi:10.1007/s00449-020-02478-3

Abstract

Bioprocess development and optimization are still cost- and time-intensive due to the enormous number of experiments involved. In this study, the recently introduced model-assisted Design of Experiments (mDoE) concept (Möller et al. in Bioproc Biosyst Eng 42(5):867, https://doi.org/10.1007/s00449-019-02089-7, 2019) was extended and implemented into a software (“mDoE-toolbox”) to significantly reduce the number of required cultivations. The application of the toolbox is exemplary shown in two case studies with Saccharomyces cerevisiae. In the first case study, a fed-batch process was optimized with respect to the pH value and linearly rising feeding rates of glucose and nitrogen source. Using the mDoE-toolbox, the biomass concentration was increased by 30% compared to previously performed experiments. The second case study was the whole-cell biocatalysis of ethyl acetoacetate (EAA) to (S)-ethyl-3-hydroxybutyrate (E3HB), for which the feeding rates of glucose, nitrogen source, and EAA were optimized. An increase of 80% compared to a previously performed experiment with similar initial conditions was achieved for the E3HB concentration.

Highlights

Biotechnology is expected to make a significant contribution to the establishment of a bio-based economy, since it offers new product manufacturing approaches and resourceefficient technologies [2, 3]
The model-assisted Design of Experiments (mDoE)-toolbox software was tested on two optimization studies with Saccharomyces cerevisiae (S1 and S2, respectively)
In case study S1, a fedbatch process was optimized to maximize the final biomass density depending on the factors pH, and linearly rising substrate feeding rates

Summary

Introduction

Biotechnology is expected to make a significant contribution to the establishment of a bio-based economy, since it offers new product manufacturing approaches and resourceefficient technologies [2, 3]. For each recommended factor combination i, the time courses of the modeled state variables (e.g., cell weight, substrate, and product concentration) are simulated multiple times (Monte Carlo simulations, Fig. 1 box IV) taking into account the previously determined parameter probability functions (box II) From these simulations, the average expected response ri (e.g., average maximal cell dry weight) and the variability i are calculated. Using the mDoE-toolbox, the available knowledge can be captured in the mathematical model, which can serve as a basis for advanced process understanding and digital twins [36,37] In this way, the new data obtained from the recommended experiments can be used to re-adapt the model parameters and their probability distribution or to modify the model structure if so far unknown effects were identified [19, 24, 28].

Experiments for modeling

Results and discussion

Conclusion

Compliance with ethical standards