Recent noteworthy advances in the development of 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 capabilities of predicting 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, more predictable models are produced.
This review highlights the applications of CBM and ML independently and
the combination of these two approaches for analyzing and optimizing
fermentation parameters.
Constraint-based modeling and machine learning applications for analysis and optimization of fermentation parameters
