Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters.

Recent noteworthy advances in developing high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its ability to predict the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, a more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. GRAPHICAL ABSTRACT LAY SUMMARY: In this study, after a brief review of recent efforts in the literature using machine learning (ML) and constraint-based modeling (CBM) to optimize fermentation parameters, the concepts of integration of these two methods are stated. ML and CBM can synergize with each other to build predictive models for analyzing and optimizing the fermentation process. The integration of CBM and ML is possible in several ways, including fluxomics analysis, multi-omics integration, fluxomics generation, genome annotation, and gap filling.