teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering.

Area of Science:

• Synthetic biology
• Computational biology
• Bioengineering

Background:

• Synthetic biology relies on data-driven engineering of biological systems.
• Handling complex biological data under FAIR principles is crucial for efficient engineering.
• Existing tools often lack user-friendly interfaces for simulation and data integration.

Purpose of the Study:

• To develop an open-source, FAIR-compliant platform for computer-aided design and analysis in synthetic biology.
• To enable user-friendly simulation, organization, and guidance for engineering biosystems.
• To apply the platform for designing, simulating, and optimizing metabolic pathways.

Main Methods:

• Development of teemi, a Python-based, literate programming platform hosted on GitHub.
• Application of teemi for designing and simulating bioengineering workflows.
• Integration and analysis of multivariate datasets using teemi.
• Utilizing machine learning for predictive engineering of metabolic pathways.

Main Results:

• Demonstrated the application of teemi in designing and simulating bioengineering tasks.
• Successfully integrated and analyzed complex, multivariate datasets.
• Applied machine learning for predictive engineering of metabolic pathways for alkaloid precursor production in yeast.
• The teemi platform is publicly available and FAIR-compliant.

Conclusions:

• The teemi platform provides a user-friendly, FAIR-compliant solution for synthetic biology data management and analysis.
• It supports the iterative design-build-test-learn cycle, enhancing the engineering of biological systems.
• teemi facilitates machine learning applications for predictive metabolic engineering, aiding in the production of valuable compounds.