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

Amir Pandi (), Christoph Diehl, Ali Yazdizadeh Kharrazi, Scott A. Scholz, Elizaveta Bobkova, Léon Faure, Maren Nattermann, David Adam, Nils Chapin, Yeganeh Foroughijabbari, Charles Moritz, Nicole Paczia, Niña Socorro Cortina, Jean-Loup Faulon and Tobias J. Erb ()
Additional contact information
Amir Pandi: Max Planck Institute for Terrestrial Microbiology
Christoph Diehl: Max Planck Institute for Terrestrial Microbiology
Ali Yazdizadeh Kharrazi: DataChef
Scott A. Scholz: Max Planck Institute for Terrestrial Microbiology
Elizaveta Bobkova: Max Planck Institute for Terrestrial Microbiology
Léon Faure: University of Paris-Saclay
Maren Nattermann: Max Planck Institute for Terrestrial Microbiology
David Adam: Max Planck Institute for Terrestrial Microbiology
Nils Chapin: Max Planck Institute for Terrestrial Microbiology
Yeganeh Foroughijabbari: Max Planck Institute for Terrestrial Microbiology
Charles Moritz: Max Planck Institute for Terrestrial Microbiology
Nicole Paczia: Max Planck Institute for Terrestrial Microbiology
Niña Socorro Cortina: Max Planck Institute for Terrestrial Microbiology
Jean-Loup Faulon: University of Paris-Saclay
Tobias J. Erb: Max Planck Institute for Terrestrial Microbiology

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: 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 1025 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.

Date: 2022
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DOI: 10.1038/s41467-022-31245-z

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