teemi

teemi facilitates simulation, organization, and guidance for iterative design-build-test-learn cycles in bioengineering of biosystems.

Key Features:

• Literate programming interface: The platform operates under a literate programming user-interface hosted on GitHub.
• Bioengineering design and simulation: Supports designing and simulating bioengineering workflows.
• Multivariate data integration and analysis: Enables integrating and analyzing multivariate datasets.
• Machine learning for predictive engineering: Implements machine-learning for predictive engineering of metabolic pathway designs.
• FAIR compliance: The platform is fully compliant with FAIR principles for findability, accessibility, interoperability, and reusability of genotype-phenotype relationship data.

Scientific Applications:

• Metabolic pathway engineering: Applied to predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast.
• Genotype-phenotype relationship analysis: Supports data-driven engineering of biocatalysis, cellular functions, and organism behavior through systematic handling of genotype-phenotype relationships.

Methodology:

teemi is implemented as a Python-based computer-aided design and analysis platform.