Exploring Biocatalysts with Microfluidics: Unlocking Machine Learning Potential

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The development of biocatalytic applications has gained momentum and is deemed a sustainable alternative to traditional chemical processes. Biocatalytic applications provide green solutions that meet the growing demand for new biotechnological products. It is critical to identify and analyze new enzyme variants suitable for industrial processes. While next-generation sequencing systems have increased throughput, the characterization of obtained candidates remains a bottleneck in identifying the desired biocatalysts. At present, only a few microfluidic systems for enzyme analysis are available commercially, and the plethora of published prototypes is yet to become a streamlined commercial platform. In this review, we explore the current trends and state-of-the-art technologies available for biocatalyst structural and functional analysis. The advantages and disadvantages of the microfluidic technologies currently available are discussed in detail, along with their readiness for routine laboratory use. We also highlight the potential of microfluidics to leverage the power of machine learning for biocatalyst development.

The article mentions a company that makes commercial microfluidic systems for enzyme analysis, although only a few are currently available. The article has not endorsed, promoted, or provided any additional information about the company.

The article did not mention any person or researcher in particular. It discusses the current trends and state-of-the-art technologies available for the analysis of biocatalysts using microfluidics.

In conclusion, microfluidic tools for biocatalyst analysis have become increasingly important in identifying enzyme variants suitable for industrial processes. Although there are a few commercial microfluidic systems available, there is a lot of unexplored potential, and numerous published prototypes are yet to become a streamlined commercial platform. Future prospects for leveraging the power of machine learning in biocatalyst development are encouraging.

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