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- W3189227095 endingPage "2100212" @default.
- W3189227095 startingPage "2100212" @default.
- W3189227095 abstract "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." @default.
- W3189227095 created "2021-08-16" @default.
- W3189227095 creator A5003253049 @default.
- W3189227095 creator A5068565973 @default.
- W3189227095 creator A5074538839 @default.
- W3189227095 creator A5075132016 @default.
- W3189227095 date "2021-09-02" @default.
- W3189227095 modified "2023-10-18" @default.
- W3189227095 title "Synergisms of machine learning and constraint‐based modeling of metabolism for analysis and optimization of fermentation parameters" @default.
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