Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida

Zournas, Apostolos; Incha, Matthew R.; Radivojevic, Tijana; Blay, Vincent; Martí, Jose Manuel; Costello, Zak; Schimdt, Matthias; Chung, Tan; Thompson, Mitchell G.; Pearson, Allison; Kinnunen, Patrick C.; Eng, Thomas; Lawson, Christopher E.; Tan, Stephen; Ogorzalek, Tadeusz; Kaplan, Nurgul; Forrer, Mark; Backman, Tyler; Mukhopadhyay, Aindrila; Hillson, Nathan J.; Keasling, Jay D.; Garcia Martin, Hector

doi:10.1038/s42003-025-08039-2

Title: Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida

Journal Article · 18 April 2025 · Communications Biology

DOI: https://doi.org/10.1038/s42003-025-08039-2 · OSTI ID:2562742

^[1]; Incha, Matthew R. ^[1]; Radivojevic, Tijana ^[1];

^[2];

^[1]; Costello, Zak ^[1];

^[2]; Chung, Tan ^[3];

^[2]; Pearson, Allison ^[2];

^[1];

^[2]; Lawson, Christopher E. ^[2]; Tan, Stephen ^[1]; Ogorzalek, Tadeusz ^[1]; Kaplan, Nurgul ^[1]; Forrer, Mark ^[4];

^[2]; Mukhopadhyay, Aindrila ^[2];

^[1] more »

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); USDOE Agile BioFoundry, Emeryville, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); University of California, Berkeley, CA (United States)
USDOE Agile BioFoundry, Emeryville, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); USDOE Agile BioFoundry, Emeryville, CA (United States); Joint BioEnergy Institute (JBEI), Emeryville, CA (United States); Basque Center for Applied Mathematics (BCAM) (Spain)

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Sandia National Laboratories (SNL-CA), Livermore, CA (United States)

Sponsoring Organization:: US Department of Energy; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Bioenergy Technologies Office (BETO); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22); USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231; NA0003525

OSTI ID:: 2562742

Report Number(s):: SAND--2025-05031J

Journal Information:: Communications Biology, Journal Name: Communications Biology Journal Issue: 1 Vol. 8; ISSN 2399-3642

Publisher:: Springer NatureCopyright Statement

Country of Publication:: United States

Language:: English

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