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

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, 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.

Development of a Digital Twin for Enzymatic Hydrolysis Processes

“…By combining the newly developed DT and model-based tools for process optimization [25,41], the enzymatic hydrolysis process to produce organic nutrient media can be systematically improved. Since both the model of the enzymatic hydrolysis processes and the DT were built generically, the DT for the enzymatic hydrolysis processes can be quickly and easily adapted to other enzymatic processes as well as other reactor configurations.…”

“…To determine the accuracy of the fit of the model after its parametrization, the coefficient of determination (R 2 ) was calculated by dividing the difference between experimental y i and simulated data y s,i with the difference between the experimental data and their mean value ӯ [25] (Equation ( 1)).…”

“…Initially, the mathematical model was written in C-eStIM, and its parameters were adjusted manually to approximate the measured data. For further parameterization, the model parameters were adapted by minimizing the weighted mean-square deviation (wMSD) using the Nelder-Mead algorithm [25,26]. The Nelder-Mead algorithm was selected because the method is effective and computationally efficient [26].…”

“…In addition, the programming environments C-eStIM [24] and R [27] used for this work, offer special Nelder-Mead packages. The wMSD was calculated from the squared difference between the measured value y m and the simulated value y s , divided by the number of data points n in the data set and multiplied by a factor for weighting individual data points k weighting [25] (Equation ( 2)).…”

Model‐based workflow for scale‐up of process strategies developed in miniaturized bioreactor systems

“…To adapt the model parameters, the objective function (weighted sum of squared residuals [RSMD]) between the simulated and experimental data for all time points and variables was minimized using the Nelder-Mead algorithm, as commonly used for model parameter identification. 3,22,23,38 Alternative approaches for model parameter identification and adaption are the Bayesian inference method 26,39,54 as well as the adaptive experimental redesign which have been discussed in the past. [40][41][42] Furthermore, model-based Design of Experiments strategies could be used to design initial experiments and identify suitable model parameters.…”