teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering.
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 biosy...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011929&type=printable |
| _version_ | 1797247967935070208 |
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| author | Søren D Petersen Lucas Levassor Christine M Pedersen Jan Madsen Lea G Hansen Jie Zhang Ahmad K Haidar Rasmus J N Frandsen Jay D Keasling Tilmann Weber Nikolaus Sonnenschein Michael K Jensen |
| author_facet | Søren D Petersen Lucas Levassor Christine M Pedersen Jan Madsen Lea G Hansen Jie Zhang Ahmad K Haidar Rasmus J N Frandsen Jay D Keasling Tilmann Weber Nikolaus Sonnenschein Michael K Jensen |
| author_sort | Søren D Petersen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T20:07:07Z |
| format | Article |
| id | doaj.art-c2a8272c0f8f4e8583e467f28cf7f764 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-24T20:07:07Z |
| publishDate | 2024-03-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-c2a8272c0f8f4e8583e467f28cf7f7642024-03-24T05:31:57ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-03-01203e101192910.1371/journal.pcbi.1011929teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering.Søren D PetersenLucas LevassorChristine M PedersenJan MadsenLea G HansenJie ZhangAhmad K HaidarRasmus J N FrandsenJay D KeaslingTilmann WeberNikolaus SonnenscheinMichael K JensenSynthetic 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.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011929&type=printable |
| spellingShingle | Søren D Petersen Lucas Levassor Christine M Pedersen Jan Madsen Lea G Hansen Jie Zhang Ahmad K Haidar Rasmus J N Frandsen Jay D Keasling Tilmann Weber Nikolaus Sonnenschein Michael K Jensen teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering. PLoS Computational Biology |
| title | teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering. |
| title_full | teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering. |
| title_fullStr | teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering. |
| title_full_unstemmed | teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering. |
| title_short | teemi: An open-source literate programming approach for iterative design-build-test-learn cycles in bioengineering. |
| title_sort | teemi an open source literate programming approach for iterative design build test learn cycles in bioengineering |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1011929&type=printable |
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