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

automation-and-machine-learning-drive-rapid-optimization-of-isoprenol-production-in-pseudomonas-putida
Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida

Data availability

Oligonucleotides, as well as plasmids and strains constructed and used in this work, are described in Supplementary Data 24 as well as Supplementary Table 2. Plasmids and strains are available in the public domain of the JBEI Registry (https://public-registry.jbei.org). Source data are provided in the Source Data file. The generated mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD063733 (DBTL0) [https://www.ebi.ac.uk/pride/archive/projects/PXD063733], PXD063737 (DBTL1) [https://www.ebi.ac.uk/pride/archive/projects/PXD063737], PXD063738 (DBTL2) [https://www.ebi.ac.uk/pride/archive/projects/PXD063738], PXD063740 (DBTL3) [https://www.ebi.ac.uk/pride/archive/projects/PXD063740], PXD063743 (DBTL4) [https://www.ebi.ac.uk/pride/archive/projects/PXD063743], PXD063744 (DBTL5) [https://www.ebi.ac.uk/pride/archive/projects/PXD063744], and PXD063746 (DBTL6) [https://www.ebi.ac.uk/pride/archive/projects/PXD063746]65. DIA-NN is freely available for download from https://github.com/vdemichev/DiaNN. All proteomics and GC-FID data were also deposited into a publicly available DRYAD database (https://doi.org/10.5061/dryad.gtht76hzh). Source data are provided with this paper.

Code availability

ART is freely available for non-commercial use by academic institutions and for a small licensing fee for commercial use; the license can be accessed at https://github.com/JBEI/ART. All code required to reproduce this study, including ART, automation workflows, and figure generation, is provided as Jupyter Notebooks and can be accessed at https://github.com/JBEI/Isoprenol_CRISPRi or https://doi.org/10.5281/zenodo.1717868466.

References

  1. French, K. E. Harnessing synthetic biology for sustainable development. Nat. Sustain. 2, 250–252 (2019).

    Google Scholar 

  2. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

    Google Scholar 

  3. Gaj, T., Gersbach, C. A. & Barbas, C. F. ZFN, TALEN, and CRISPR/Cas-based methods for genome engineering. Trends Biotechnol. 31, 397–405 (2013).

    Google Scholar 

  4. Wetmore, K. M. et al. Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons. mBio 6, e00306–e00315 (2015).

    Google Scholar 

  5. Romero, P. A. & Arnold, F. H. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 10, 866–876 (2009).

    Google Scholar 

  6. Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    Google Scholar 

  7. Voigt, C. A. Synthetic biology 2020-2030: six commercially-available products that are changing our world. Nat. Commun. 11, 6379 (2020).

    Google Scholar 

  8. Paddon, C. J. & Keasling, J. D. Semi-synthetic artemisinin: a model for the use of synthetic biology in pharmaceutical development. Nat. Rev. Micro 12, 355–367 (2014).

    Google Scholar 

  9. Meng, F. & Ellis, T. The second decade of synthetic biology: 2010-2020. Nat. Commun. 11, 5174 (2020).

    Google Scholar 

  10. Keasling, J. et al. Microbial production of advanced biofuels. Nat. Rev. Microbiol. 19, 701–715 (2021).

    Google Scholar 

  11. Rosales Calderon, O. et al. Sustainable Aviation Fuel (SAF) State-of-Industry Report: State of SAF Production Process. Rep. NREL/TP-5100-87802 (National Renewable Energy Laboratory, 2024).

  12. Baral, N. R. et al. Production cost and carbon footprint of biomass-derived dimethylcyclooctane as a high-performance jet fuel blendstock. ACS Sustain. Chem. Eng. 9, 11872–11882 (2021).

    Google Scholar 

  13. Walkling, C. J., Zhang, D. D. & Harvey, B. G. Extended fuel properties of sustainable aviation fuel blends derived from linalool and isoprene. Fuel 356, 129554 (2024).

    Google Scholar 

  14. Stephenson, A. et al. Physical laboratory automation in synthetic biology. ACS Synth. Biol. 12, 3156–3169 (2023).

    Google Scholar 

  15. Gurdo, N., Volke, D. C., McCloskey, D. & Nikel, P. I. Automating the design-build-test-learn cycle towards next-generation bacterial cell factories. N. Biotechnol. 74, 1–15 (2023).

    Google Scholar 

  16. Zhang, J. et al. Accelerating strain engineering in biofuel research via build and test automation of synthetic biology. Curr. Opin. Biotechnol. 67, 88–98 (2021).

    Google Scholar 

  17. Chao, R., Mishra, S., Si, T. & Zhao, H. Engineering biological systems using automated biofoundries. Metab. Eng. 42, 98–108 (2017).

    Google Scholar 

  18. Keasling, J. D. Synthetic biology and the development of tools for metabolic engineering. Metab. Eng. 14, 189–195 (2012).

    Google Scholar 

  19. Lee, S. Y. & Kim, H. U. Systems strategies for developing industrial microbial strains. Nat. Biotechnol. 33, 1061–1072 (2015).

    Google Scholar 

  20. Pandi, A. et al. A versatile active learning workflow for optimization of genetic and metabolic networks. Nat. Commun. 13, 3876 (2022).

    Google Scholar 

  21. Zhang, J. et al. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. Nat. Commun. 11, 4880 (2020).

    Google Scholar 

  22. Kumar, P. et al. Active and machine learning-based approaches to rapidly enhance microbial chemical production. Metab. Eng. 67, 216–226 (2021).

    Google Scholar 

  23. Koscher, B. A. et al. Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back. Science 382, eadi1407 (2023).

    Google Scholar 

  24. Angello, N. H. et al. Closed-loop optimization of general reaction conditions for heteroaryl Suzuki-Miyaura coupling. Science 378, 399–405 (2022).

    Google Scholar 

  25. Esterhuizen, J. A., Mathur, A., Goldsmith, B. R. & Linic, S. High-performance iridium-molybdenum oxide electrocatalysts for water oxidation in acid: Bayesian optimization discovery and experimental testing. J. Am. Chem. Soc. 146, 5511–5522 (2024).

    Google Scholar 

  26. Zournas, A. et al. Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida. Commun. Biol. 8, 630 (2025).

    Google Scholar 

  27. Jessop-Fabre, M. M. & Sonnenschein, N. Improving reproducibility in synthetic biology. Front. Bioeng. Biotechnol. 7, 18 (2019).

    Google Scholar 

  28. Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016).

    Google Scholar 

  29. Morrell, W. C. et al. The experiment data depot: a web-based software tool for biological experimental data storage, sharing, and visualization. ACS Synth. Biol. 6, 2248–2259 (2017).

    Google Scholar 

  30. Pandit, A. V., Srinivasan, S. & Mahadevan, R. Redesigning metabolism based on orthogonality principles. Nat. Commun. 8, 15188 (2017).

    Google Scholar 

  31. Banerjee, D. et al. Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida. Metab. Eng. 82, 157–170 (2024).

    Google Scholar 

  32. Gaillard, W. R. et al. High-Throughput Microfluidic Electroporation (HTME): a scalable, 384-well platform for multiplexed cell engineering. Bioengineering 12, 788 (2025).

    Google Scholar 

  33. Unthan, S., Radek, A., Wiechert, W., Oldiges, M. & Noack, S. Bioprocess automation on a Mini Pilot Plant enables fast quantitative microbial phenotyping. Microb. Cell Fact. 14, 216 (2015).

    Google Scholar 

  34. Czajka, J. J. et al. FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets. Bioinformatics 41, btaf471 (2025).

    Google Scholar 

  35. Wang, X. et al. Engineering isoprenoids production in metabolically versatile microbial host Pseudomonas putida. Biotechnol. Biofuels Bioprod. 15, 137 (2022).

    Google Scholar 

  36. Yunus, I. S. et al. Predictive CRISPR-mediated gene downregulation for enhanced production of sustainable aviation fuel precursor in Pseudomonas putida. Metab. Eng. https://doi.org/10.1016/j.ymben.2025.11.007 (2025).

  37. Reis, A. C. et al. Simultaneous repression of multiple bacterial genes using nonrepetitive extra-long sgRNA arrays. Nat. Biotechnol. 37, 1294–1301 (2019).

    Google Scholar 

  38. Peters, J. M. et al. A comprehensive, CRISPR-based functional analysis of essential genes in bacteria. Cell 165, 1493–1506 (2016).

    Google Scholar 

  39. Dong, C., Fontana, J., Patel, A., Carothers, J. M. & Zalatan, J. G. Synthetic CRISPR-Cas gene activators for transcriptional reprogramming in bacteria. Nat. Commun. 9, 2489 (2018).

    Google Scholar 

  40. Shin, J. et al. Genome Engineering of Eubacterium limosum Using Expanded Genetic Tools and the CRISPR-Cas9 System. ACS Synth. Biol. 8, 2059–2068 (2019).

    Google Scholar 

  41. Radivojević, T., Costello, Z., Workman, K. & Garcia Martin, H. A machine learning automated recommendation tool for synthetic biology. Nat. Commun. 11, 4879 (2020).

    Google Scholar 

  42. Morales, G., Ugidos, A. & Rojo, F. Inactivation of the Pseudomonas putida cytochrome o ubiquinol oxidase leads to a significant change in the transcriptome and to increased expression of the CIO and cbb3-1 terminal oxidases. Environ. Microbiol. 8, 1764–1774 (2006).

    Google Scholar 

  43. Concordet, J.-P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).

    Google Scholar 

  44. Cui, L. et al. A CRISPRi screen in E. coli reveals sequence-specific toxicity of dCas9. Nat. Commun. 9, 1912 (2018).

    Google Scholar 

  45. Rostain, W. et al. Cas9 off-target binding to the promoter of bacterial genes leads to silencing and toxicity. Nucleic Acids Res. 51, 3485–3496 (2023).

    Google Scholar 

  46. Price, M. N. et al. Mutant phenotypes for thousands of bacterial genes of unknown function. Nature 557, 503–509 (2018).

    Google Scholar 

  47. Thompson, M. G. et al. Fatty acid and alcohol metabolism in Pseudomonas putida: functional analysis using random barcode transposon sequencing. Appl. Environ. Microbiol. 86, e01665–20 (2020).

    Google Scholar 

  48. Hédou, J. et al. Discovery of sparse, reliable omic biomarkers with Stabl. Nat. Biotechnol. 42, 1581–1593 (2024).

    Google Scholar 

  49. Lim, H. G. et al. Evolution guided tolerance engineering of Pseudomonas putida KT2440 for production of the sustainable aviation fuel precursor isoprenol. Metab. Eng. 91, 322–335 (2025).

  50. Iwai, K. et al. Scalable and automated CRISPR-based strain engineering using droplet microfluidics. Microsyst. Nanoeng. 8, 31 (2022).

    Google Scholar 

  51. Kang, A. et al. Isopentenyl diphosphate (IPP)-bypass mevalonate pathways for isopentenol production. Metab. Eng. 34, 25–35 (2016).

    Google Scholar 

  52. Kang, A. et al. Optimization of the IPP-bypass mevalonate pathway and fed-batch fermentation for the production of isoprenol in Escherichia coli. Metab. Eng. 56, 85–96 (2019).

    Google Scholar 

  53. Storch, M. et al. BASIC: A new biopart assembly standard for idempotent cloning provides accurate, single-tier DNA assembly for synthetic biology. ACS Synth. Biol. 4, 781–787 (2015).

    Google Scholar 

  54. Funke, M. et al. Microfluidic biolector-microfluidic bioprocess control in microtiter plates. Biotechnol. Bioeng. 107, 497–505 (2010).

    Google Scholar 

  55. Rienzo, M. et al. High-throughput screening for high-efficiency small-molecule biosynthesis. Metab. Eng. 63, 102–125 (2021).

    Google Scholar 

  56. Chen, Y. et al. Alkaline-SDS cell lysis of microbes with acetone protein precipitation for proteomic sample preparation in 96-well plate format. PLoS ONE 18, e0288102 (2023).

    Google Scholar 

  57. Chen, Y., Gin, J. & Petzold, C. J. Discovery proteomic (DIA) LC-MS/MS data acquisition and analysis v2. Protocols.io https://doi.org/10.17504/protocols.io.e6nvwk1z7vmk/v2 (2022).

  58. Demichev, V., Messner, C. B., Vernardis, S. I., Lilley, K. S. & Ralser, M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods 17, 41–44 (2020).

    Google Scholar 

  59. Ahrné, E., Molzahn, L., Glatter, T. & Schmidt, A. Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics 13, 2567–2578 (2013).

    Google Scholar 

  60. Silva, J. C., Gorenstein, M. V., Li, G.-Z., Vissers, J. P. C. & Geromanos, S. J. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol. Cell Proteom. 5, 144–156 (2006).

    Google Scholar 

  61. Ribeiro, P. et al. in Genetic Programming Theory and Practice XX (eds Winkler, S. et al.) 1–17 (Springer Nature Singapore, 2024).

  62. Barber, R. F. & Candès, E. J. Controlling the false discovery rate via knockoffs. Ann. Stat. 43, 2055–2085 (2015).

    Google Scholar 

  63. Czajka, J. J. et al. Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production. Metab. Eng. Commun. 15, e00206 (2022).

    Google Scholar 

  64. Wannier, T. M. et al. Improved bacterial recombineering by parallelized protein discovery. Proc. Natl. Acad. Sci. USA 117, 13689–13698 (2020).

    Google Scholar 

  65. Perez-Riverol, Y. et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50, D543–D552 (2022).

    Google Scholar 

  66. Carruthers, D. N. et al. Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida. JBEI/Isoprenol_CRISPRi. Zenodo https://doi.org/10.5281/zenodo.17178684 (2025).

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Acknowledgements

We would like to thank Dr. Thomas Eng, who supplied protocols for gene deletion as well as several plasmids and oligonucleotides used for gene deletions in previous P. putida work. We also appreciate the expertise shared by Dr. Joonhoon Kim, who introduced us to Biocluster in R and provided insights into exploring off-target effects. This work was part of the DOE Joint BioEnergy Institute (http://www.jbei.org) supported by the U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, through contract DE-AC0205CH11231 between Lawrence Berkeley National Laboratory and the U.S. Department of Energy. All authors were supported on this contract. The United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The views and opinions expressed by the authors do not necessarily reflect those of the United States Government or any of its agencies. The United States Government and its employees make no warranty, expressed or implied, regarding the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor do they represent that the use of such information would not infringe privately owned rights.

Author information

Author notes

  1. These authors contributed equally: David N. Carruthers, Patrick C. Kinnunen.

  2. These authors jointly supervised this work: Hector Garcia Martin, Taek Soon Lee.

Authors and Affiliations

  1. Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    David N. Carruthers, Patrick C. Kinnunen, Yan Chen, Jennifer W. Gin, Ian S. Yunus, Stephen Tan, Tijana Radivojevic, Christopher J. Petzold, Aindrila Mukhopadhyay, Hector Garcia Martin & Taek Soon Lee

  2. Joint BioEnergy Institute, Emeryville, CA, USA

    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, Anup K. Singh, Jess Sustarich, Christopher J. Petzold, Aindrila Mukhopadhyay, Hector Garcia Martin & Taek Soon Lee

  3. Lawrence Livermore National Laboratory, Livermore, CA, USA

    Yuerong Li & Anup K. Singh

  4. Sandia National Laboratories, Livermore, CA, USA

    William R. Galliard & Jess Sustarich

  5. Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Paul D. Adams & Hector Garcia Martin

  6. Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA

    Paul D. Adams

Authors

  1. David N. Carruthers
  2. Patrick C. Kinnunen
  3. Yuerong Li
  4. Yan Chen
  5. Jennifer W. Gin
  6. Ian S. Yunus
  7. William R. Galliard
  8. Stephen Tan
  9. Tijana Radivojevic
  10. Paul D. Adams
  11. Anup K. Singh
  12. Jess Sustarich
  13. Christopher J. Petzold
  14. Aindrila Mukhopadhyay
  15. Hector Garcia Martin
  16. Taek Soon Lee

Contributions

D.N.C., P.C.K., I.S.Y., H.G.M., and T.S.L. conceptualized the study. Y.C. and J.W.G. collected and curated proteomics data. P.C.K. wrote code to use ART. D.N.C., and S.T. developed the automation workflow. D.N.C. and Y.L. performed the biological experiments. W.R.G. and J.S. developed the HTME, and W.R.G. performed the electroporations. D.N.C., P.C.K., and T.R. performed data analysis. P.D.A., A.K.S., C.J.P., A.M., H.G.M., and T.S.L., secured funding and resources. All authors revised and approved the manuscript.

Corresponding authors

Correspondence to Hector Garcia Martin or Taek Soon Lee.

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Competing interests

A.K.S. and J.S. are founders of XLSI Bio. H.G.M. and W.R.G. are scientific advisors to XLSI Bio. The remaining authors declare no competing interests.

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Nature Communications thanks Paul Jensen and the other anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

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Carruthers, D.N., Kinnunen, P.C., Li, Y. et al. Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida. Nat Commun (2025). https://doi.org/10.1038/s41467-025-66304-8

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