A versatile active learning workflow for optimization of genetic and metabolic 
networks

Pandi, A.; Diehl, C.; Yazdizadeh Kharrazi, A.; Scholz, S. A.; Bobkova, E.; Faure, L.; Nattermann, M.; Adam, D.; Chapin, N.; Foroughijabbari, Y.; Moritz, C.; Paczia, N.; Cortina, N. S.; Faulon, J. L.; Erb, T. J.

doi:10.1038/s41467-022-31245-z

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A versatile active learning workflow for optimization of genetic and metabolic networks

MPS-Authors

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Pandi, A.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons254217

Diehl, C.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

Scholz, S. A.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons255539

Bobkova, E.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons273300

Nattermann, M.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

Adam, D.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

Chapin, N.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

Foroughijabbari, Y.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

Moritz, C.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons261240

Paczia, N.
Core Facility Metabolomics and small Molecules Mass Spectrometry, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons254199

Cortina, N. S.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;

/persons/resource/persons254247

Erb, T. J.
Understanding and Building Metabolism, Department of Biochemistry and Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Max Planck Society;
Center for Synthetic Microbiology (SYNMIKRO), Philipps University of Marburg, Marburg;

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https://doi.org/10.1038/s41467-022-31245-z
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Citation

Pandi, A., Diehl, C., Yazdizadeh Kharrazi, A., Scholz, S. A., Bobkova, E., Faure, L., et al. (2022). A versatile active learning workflow for optimization of genetic and metabolic networks. Nature Communications, 13(1): 3876. doi:10.1038/s41467-022-31245-z.

Cite as: https://hdl.handle.net/21.11116/0000-000A-B2A1-6

Abstract

Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, we describe METIS, a versatile active machine learning workflow with a simple online interface for the data-driven optimization of biological targets with minimal experiments. We demonstrate our workflow for various applications, including cell-free transcription and translation, genetic circuits, and a 27-variable synthetic CO2-fixation cycle (CETCH cycle), improving these systems between one and two orders of magnitude. For the CETCH cycle, we explore 10(25) conditions with only 1,000 experiments to yield the most efficient CO2-fixation cascade described to date. Beyond optimization, our workflow also quantifies the relative importance of individual factors to the performance of a system identifying unknown interactions and bottlenecks. Overall, our workflow opens the way for convenient optimization and prototyping of genetic and metabolic networks with customizable adjustments according to user experience, experimental setup, and laboratory facilities.