Evidence map›Paper›PMID 37956196›Full record

ArticlePloS one2023

Enabling AI in synthetic biology through Construction File specification.

Nassim Ataii, Sanjyot Bakshi, Yisheng Chen, Michael Fernandez, Zihang Shao, Zachary Scheftel, Connor Tou, Mia Vega, Yuting Wang, Hanxiao Zhang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Nassim AtaiiDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Sanjyot BakshiDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Yisheng ChenDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Michael FernandezDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Zihang ShaoDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Zachary ScheftelDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Connor TouDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Mia VegaDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Yuting WangDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Hanxiao ZhangDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.
Zexuan ZhaoDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.ORCID 0000-0003-2410-0893
J Christopher AndersonDepartment of Bioengineering, University of California, Berkeley, Berkeley, California, United States of America.ORCID 0000-0002-9115-7323

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceSynthetic BiologyAutomationComputer SimulationHumansSoftware

Identifiers

PMID37956196
PMCPMC10642840

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.