Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis.

Area of Science:

• Biocatalysis and biochemical engineering.
• Data science applications in chemistry.
• Enzyme kinetics and reaction engineering.

Background:

• High-throughput experimentation in biocatalysis generates vast datasets.
• Data-driven approaches are essential for mechanistic and predictive modeling of biocatalysts and bioprocesses.
• Understanding enzymatic reaction systems aids in overcoming kinetic bottlenecks and optimizing thermodynamics.

Purpose of the Study:

• To highlight the increasing role of data science in biocatalysis.
• To emphasize the need for FAIR data principles in biocatalysis research.
• To outline the requirements for developing automated laboratory infrastructures.

Main Methods:

• Utilizing high-throughput experimentation for data generation.
• Applying mechanistic and data-driven modeling approaches.
• Implementing FAIR data principles through standardized formats, reproducible documentation, collaborative platforms, and data repositories.

Main Results:

• Biocatalysis is increasingly reliant on data science and computational modeling.
• FAIR data principles are essential for collaborative research and data sharing.
• Standardized data, reproducible methods, and collaborative platforms facilitate scientific advancement.

Conclusions:

• The FAIRification of biocatalysis data is a community-driven effort.
• Achieving FAIR data is a prerequisite for automated laboratory infrastructures.
• This approach enhances reproducibility and reduces development time and costs for novel synthesis routes.