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

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; Jensen, Michael K.

doi:10.1371/journal.pcbi.1011929

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

Journal Article · 07 March 2024 · PLoS Computational Biology (Online)

DOI: https://doi.org/10.1371/journal.pcbi.1011929 · OSTI ID:2470853

^[1];

^[1]; Pedersen, Christine M. ^[1]; Madsen, Jan ^[1]; Hansen, Lea G. ^[1]; Zhang, Jie ^[1]; Haidar, Ahmad K. ^[1]; Frandsen, Rasmus J. N. ^[1]; Keasling, Jay D. ^[2]; Weber, Tilmann ^[1]; Sonnenschein, Nikolaus ^[3];

^[1]

Technical Univ. of Denmark, Lyngby (Denmark)
Technical Univ. of Denmark, Lyngby (Denmark); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States); Shenzhen Institutes of Advanced Technologies (China)
Technical Univ. of Denmark, Lyngby (Denmark); Ginkgo Bioworks, Boston, MA (United States)

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: European Union Horizon 2020; Novo Nordisk Foundation; USDOE

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2470853

Journal Information:: PLoS Computational Biology (Online), Journal Name: PLoS Computational Biology (Online) Journal Issue: 3 Vol. 20; ISSN 1553-7358

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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

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