Optimization and Scale-Up of Fermentation Processes Driven by Models

Abstract

In the era of sustainable development, the use of cell factories to produce various compounds by fermentation has attracted extensive attention; however, industrial fermentation requires not only efficient production strains, but also suitable extracellular conditions and medium components, as well as scaling-up. In this regard, the use of biological models has received much attention, and this review will provide guidance for the rapid selection of biological models. This paper first introduces two mechanistic modeling methods, kinetic modeling and constraint-based modeling (CBM), and generalizes their applications in practice. Next, we review data-driven modeling based on machine learning (ML), and highlight the application scope of different learning algorithms. The combined use of ML and CBM for constructing hybrid models is further discussed. At the end, we also discuss the recent strategies for predicting bioreactor scale-up and culture behavior through a combination of biological models and computational fluid dynamics (CFD) models.

Development of a novel microplate for high-throughput screening and optimization of DHA producing strains based on CFD technology

“…The seed medium was artificial seawater supplemented with glucose (50 g/L) and yeast extract (0.4 g/L). The fermentation medium was artificial seawater containing 70 g/L glucose, 1 g/L yeast extract, and 30 g/L glutamate [1].…”

“…Over the decades, microorganisms have been used as "mini-factories" in biomanufacturing to aid in the diversity of metabolic pathways and their accompanying ability to transform a wide range of renewable raw materials into value-added compounds via fermentation [1][2][3]. In the quest to obtain DHA from fish oil, species like Schizochytriumsp.…”

Precision fermentation with mass spectrometry‐based spent media analysis

“…Finally, to enable efficient scaleup of the bioprocess, it is also recommended to combine the metabolic model with a computational fluid dynamics model that maps the effect of the fermentation environment on the microbial culture (Du et al, 2022).…”

“…Modeling a bioprocess is also useful for efficient optimization and scaleup. The exometabolome data can be modeled for simultaneous and sequential substrate consumptions, using mechanistic, data‐driven, or hybrid modeling approaches (Du et al, 2022). A constraint‐based flux balance analysis (FBA) model built on time‐resolved exometabolome data showed the ability to predict metabolic states and dynamic flux rearrangements during the growth of E. coli in complex nutritional environments (Zampieri et al, 2019).…”

Systems biology and metabolic modeling for cultivated meat: A promising approach for cell culture media optimization and cost reduction

“…The presence of these gradients has been demonstrated by measuring or taking samples at multiple locations in large‐scale bioreactors, and sensor technologies have been developed to be able to capture these nuances with the intent of improving the predictive power of computational fluid dynamics (CFD) models (Bisgaard et al., 2020). Current modeling efforts aiming to simulate the reactions inside bioreactors integrate both the use of CFD software to model complex hydrodynamics and try to gain an understanding of the metabolism of the cellular population inside the fluid (Pigou et al., 2017) because each cell inside a bioreactor can be seen as its own subsystem of metabolic and signaling networks (Du et al., 2022). In 2015, a (PBM) was introduced to account for the integration of concentration gradients and the dynamic adaptation of cells to the changing microenvironments they experience along their trajectories inside a bioreactor.…”