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Enabling AI in synthetic biology through Construction File specification
The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642840/ https://www.ncbi.nlm.nih.gov/pubmed/37956196 http://dx.doi.org/10.1371/journal.pone.0294469 |
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author | Ataii, Nassim Bakshi, Sanjyot Chen, Yisheng Fernandez, Michael Shao, Zihang Scheftel, Zachary Tou, Connor Vega, Mia Wang, Yuting Zhang, Hanxiao Zhao, Zexuan Anderson, J. Christopher |
author_facet | Ataii, Nassim Bakshi, Sanjyot Chen, Yisheng Fernandez, Michael Shao, Zihang Scheftel, Zachary Tou, Connor Vega, Mia Wang, Yuting Zhang, Hanxiao Zhao, Zexuan Anderson, J. Christopher |
author_sort | Ataii, Nassim |
collection | PubMed |
description | The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases ’Experiment’ objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively. |
format | Online Article Text |
id | pubmed-10642840 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106428402023-11-14 Enabling AI in synthetic biology through Construction File specification Ataii, Nassim Bakshi, Sanjyot Chen, Yisheng Fernandez, Michael Shao, Zihang Scheftel, Zachary Tou, Connor Vega, Mia Wang, Yuting Zhang, Hanxiao Zhao, Zexuan Anderson, J. Christopher PLoS One Research Article The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases ’Experiment’ objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively. Public Library of Science 2023-11-13 /pmc/articles/PMC10642840/ /pubmed/37956196 http://dx.doi.org/10.1371/journal.pone.0294469 Text en © 2023 Ataii et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Ataii, Nassim Bakshi, Sanjyot Chen, Yisheng Fernandez, Michael Shao, Zihang Scheftel, Zachary Tou, Connor Vega, Mia Wang, Yuting Zhang, Hanxiao Zhao, Zexuan Anderson, J. Christopher Enabling AI in synthetic biology through Construction File specification |
title | Enabling AI in synthetic biology through Construction File specification |
title_full | Enabling AI in synthetic biology through Construction File specification |
title_fullStr | Enabling AI in synthetic biology through Construction File specification |
title_full_unstemmed | Enabling AI in synthetic biology through Construction File specification |
title_short | Enabling AI in synthetic biology through Construction File specification |
title_sort | enabling ai in synthetic biology through construction file specification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10642840/ https://www.ncbi.nlm.nih.gov/pubmed/37956196 http://dx.doi.org/10.1371/journal.pone.0294469 |
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