Evidence map›Paper›PMID 41390487›Full record

ArticleNature communications2025

Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida.

David N Carruthers, Patrick C Kinnunen, Yuerong Li, Yan Chen, Jennifer W Gin, Ian S Yunus, William R Galliard, Stephen Tan, Tijana Radivojevic, Paul D Adams and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. 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. Review
  3. Article
  4. Article
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

16 authors.

David N Carruthers *Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Patrick C Kinnunen *Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-1741-2867
Yuerong LiJoint BioEnergy Institute, Emeryville, CA, USA.
Yan ChenBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Jennifer W GinBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Ian S YunusBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-2395-0821
William R GalliardJoint BioEnergy Institute, Emeryville, CA, USA.
Stephen TanBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Tijana RadivojevicBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-7165-2909
Paul D AdamsJoint BioEnergy Institute, Emeryville, CA, USA.ORCID http://orcid.org/0000-0001-9333-8219
Anup K SinghJoint BioEnergy Institute, Emeryville, CA, USA.
Jess SustarichJoint BioEnergy Institute, Emeryville, CA, USA.
Christopher J PetzoldBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-8270-5228
Aindrila MukhopadhyayBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.ORCID http://orcid.org/0000-0002-6513-7425
Hector Garcia MartinBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. hgmartin@lbl.gov.ORCID http://orcid.org/0000-0002-4556-9685
Taek Soon LeeBiological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. tslee@lbl.gov.ORCID http://orcid.org/0000-0002-0764-2626

Funding

U.S. Department of Energy (DOE) DE-AC0205CH11231
6 · The paper itself

Abstract

Advances in genome engineering have improved our ability to perturb microbial metabolic networks, yet bioproduction campaigns often struggle with parsing complex metabolic datasets to efficiently enhance product titers. We address this challenge by coupling laboratory automation with machine learning to systematically optimize the production of isoprenol, a sustainable aviation fuel precursor, in Pseudomonas putida. The simultaneous downregulation through CRISPR interference of combinations of up to four gene targets, guided by machine learning, permitted us to increase isoprenol titer 5-fold in six consecutive design-build-test-learn cycles. Moreover, machine learning enabled us to swiftly explore a vast experimental design space of 800,000 possible combinations by strategically recommending approximately 400 priority constructs. High-throughput proteomics allowed us to validate CRISPRi downregulation and identify biological mechanisms driving production increases. Our work demonstrates that ML-driven automated design-build-test-learn cycles, when combined with rigorous data validation, can rapidly enhance titers without specific biological knowledge, suggesting that it can be applied to any host, product, or pathway.

Indexed as

HemiterpenesMachine LearningMetabolic EngineeringPseudomonas putidaAutomationCRISPR-Cas SystemsMetabolic Networks and PathwaysProteomicsHemiterpenes

Identifiers

PMID41390487
PMCPMC12748988

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.