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

• Published in: PLoS computational biology Vol. 20; no. 3; p. e1011929
• Main Authors: Petersen, Søren D, Levassor, Lucas, Pedersen, Christine M, Madsen, Jan, Hansen, Lea G, Zhang, Jie, Haidar, Ahmad K, Frandsen, Rasmus J N, Keasling, Jay D, Weber, Tilmann, Sonnenschein, Nikolaus, K Jensen, Michael
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.03.2024; Public Library of Science (PLoS)
• Subjects: Analysis; Aquariums; Biochemistry & Molecular Biology; Bioengineering; Biology; Biology and Life Sciences; Biotechnology; CAD; Catalysis; Computer aided design; Computer and Information Sciences; Computer industry; Cytochrome; Data analysis; Design; Documentation; Engineering and Technology; Enzymes; Genotypes; Laboratories; Language; Machine learning; Mathematical & Computational Biology; Medical research; Metabolic engineering; Metabolic pathways; Metabolism; Methods; Natural language; Open source software; Oxidation; Phenotypes; Public software; Python; Research and Analysis Methods; Robotics; Science Policy; Social Sciences; Synthetic biology; Yeast; Yeasts; United Kingdom
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1011929

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub.