LDBT instead of DBTL: combining machine learning and rapid cell-free testing
Synthetic biology is defined by Design-Build-Test-Learn cycles. Recent advances in machine learning are changing the landscape; thus, we propose that “Learning” can precede “Design”. Moreover, adopting cell-free platforms can further accelerate “Building” and “Testing” for megascale data generation and models.
Authors
- John T. Lazar (ORCID: https://orcid.org/0000-0003-2650-8614)
- Alia Clark‐ElSayed (ORCID: https://orcid.org/0000-0001-9602-4231)
- Michael C. Jewett (ORCID: https://orcid.org/0000-0003-2948-6211)
- Andrew D. Ellington (ORCID: https://orcid.org/0000-0001-6246-5338)
- Mari Olsen (ORCID: https://orcid.org/0000-0002-8894-4027)
- Ian T. Harrison (ORCID: https://orcid.org/0009-0005-6028-2809)
Institutions
- Northwestern University (US)
- The University of Texas at Austin (US)
- Stanford University (US)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-11-05
- DOI
- https://doi.org/10.1038/s41467-025-65281-2
- Citations
- 2
- Primary Topic
- Gene Regulatory Network Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- National Science Foundation
- U.S. Department of Energy
- National Research Foundation
- National Research Foundation of Korea
- National Institutes of Health
- National Institute of General Medical Sciences
- Division of Chemical, Bioengineering, Environmental, and Transport Systems
- Army Research Office