Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida
Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still hindered by a lack of predictive capabilities. Media optimization is a critical, and often overlooked, process which is essential to obtain the titers, rates and yields needed for commercial viability. Here, we present a molecule- and host-agnostic active learning process for media optimization that is enabled by a fast and highly repeatable semi-automated pipeline. Its application yielded 60% and 70% increases in titer, and 350% increase in process yield in three different campaigns for flaviolin production in Pseudomonas putida KT2440. Explainable Artificial Intelligence techniques pinpointed that, surprisingly, common salt (NaCl) is the most important component influencing production. The optimal salt concentration is very high, comparable to seawater and close to the limits that P. putida can tolerate. The availability of fast Design-Build-Test-Learn (DBTL) cycles allowed us to show that performance improvements for active learning are rarely monotonous. This work illustrates how machine learning and automation can change the paradigm of current synthetic biology research to make it more effective and informative, and suggests a cost-effective and underexploited strategy to facilitate the high titers, rates and yields essential for commercial viability.
Authors
- Jay D. Keasling (ORCID: https://orcid.org/0000-0003-4170-6088)
- Héctor García Martín (ORCID: https://orcid.org/0000-0002-4556-9685)
- Mark Forrer
- Matthew R. Incha (ORCID: https://orcid.org/0000-0003-4122-3342)
- Aindrila Mukhopadhyay (ORCID: https://orcid.org/0000-0002-6513-7425)
- Zak Costello
- Tyler W. H. Backman (ORCID: https://orcid.org/0000-0002-6056-353X)
- Patrick C. Kinnunen (ORCID: https://orcid.org/0000-0002-1741-2867)
- Tijana Radivojević (ORCID: https://orcid.org/0000-0002-7165-2909)
- Vincent Blay (ORCID: https://orcid.org/0000-0001-9602-2375)
- Apostolos Zournas (ORCID: https://orcid.org/0000-0003-0565-6781)
- Nathan J. Hillson (ORCID: https://orcid.org/0000-0002-9169-3978)
- Nurgul Kaplan
- Christopher E. Lawson (ORCID: https://orcid.org/0000-0002-3473-1640)
- Tadeusz L. Ogorzalek
- Thomas Eng (ORCID: https://orcid.org/0000-0002-4974-3863)
- Allison S. Pearson (ORCID: https://orcid.org/0000-0001-8231-0149)
- Jose Manuel Martí (ORCID: https://orcid.org/0000-0002-1902-9749)
- Matthias Schmidt (ORCID: https://orcid.org/0000-0002-3363-4777)
- Mitchell G. Thompson (ORCID: https://orcid.org/0000-0002-1490-8074)
- Tan Chung
Institutions
- Agilent Technologies (United States) (US)
- Lawrence Berkeley National Laboratory (US)
- Sandia National Laboratories California (US)
- Basque Center for Applied Mathematics (ES)
- Joint BioEnergy Institute (US)
- University of California, Berkeley (US)
Publication Details
- Journal
- Communications Biology
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1038/s42003-025-08039-2
- Citations
- 10
- Primary Topic
- Microbial Natural Products and Biosynthesis
- Type
- article
- Field-Weighted Citation Impact
- 13.94
Funders
- U.S. Department of Energy
- Eusko Jaurlaritza
- Ministerio de Ciencia e Innovación
- Basque Center for Applied Mathematics
- Office of Science
- National Nuclear Security Administration
- Agencia Estatal de Investigación
- Biological and Environmental Research
- Sandia National Laboratories
- Lawrence Berkeley National Laboratory