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Pandi A, Diehl C, Yazdizadeh Kharrazi A, Scholz SA, Bobkova E, Faure L, Nattermann M, Adam D, Chapin N, Foroughijabbari Y, Moritz C, Paczia N, Cortina NS, Faulon JL, Erb TJ. A versatile active learning workflow for optimization of genetic and metabolic networks. Nat Commun 2022; 13:3876. [PMID: 35790733 PMCID: PMC9256728 DOI: 10.1038/s41467-022-31245-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 06/10/2022] [Indexed: 11/13/2022] Open
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. Optimization of biological networks is often limited by wet lab labor and cost, and the lack of convenient computational tools. Here, aimed at democratization and standardization, the authors describe METIS, a modular and versatile active machine learning workflow with a simple online interface for the optimization of biological target functions with minimal experimental datasets.
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Affiliation(s)
- Amir Pandi
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
| | - Christoph Diehl
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | | | - Scott A Scholz
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Elizaveta Bobkova
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Léon Faure
- Micalis Institute, INRAE, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France
| | - Maren Nattermann
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - David Adam
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Nils Chapin
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Yeganeh Foroughijabbari
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Charles Moritz
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Nicole Paczia
- Core Facility for Metabolomics and Small Molecule Mass Spectrometry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany
| | - Niña Socorro Cortina
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.,LiVeritas Biosciences, Inc., 432N Canal St.; Ste. 20, South San Francisco, CA, 94080, USA
| | - Jean-Loup Faulon
- Micalis Institute, INRAE, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France.,Genomique Metabolique, Genoscope, Institut Francois Jacob, CEA, CNRS, Univ Evry, University of Paris-Saclay, Evry, France.,Manchester Institute of Biotechnology, SYNBIOCHEM center, School of Chemistry, The University of Manchester, Manchester, UK
| | - Tobias J Erb
- Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany. .,SYNMIKRO Center of Synthetic Microbiology, Marburg, Germany.
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Fil J, Dalchau N, Chu D. Programming Molecular Systems To Emulate a Learning Spiking Neuron. ACS Synth Biol 2022; 11:2055-2069. [PMID: 35622431 PMCID: PMC9208023 DOI: 10.1021/acssynbio.1c00625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
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Hebbian theory seeks
to explain how the neurons in the brain adapt
to stimuli to enable learning. An interesting feature of Hebbian learning
is that it is an unsupervised method and, as such, does not require
feedback, making it suitable in contexts where systems have to learn
autonomously. This paper explores how molecular systems can be designed
to show such protointelligent behaviors and proposes the first chemical
reaction network (CRN) that can exhibit autonomous Hebbian learning
across arbitrarily many input channels. The system emulates a spiking
neuron, and we demonstrate that it can learn statistical biases of
incoming inputs. The basic CRN is a minimal, thermodynamically plausible
set of microreversible chemical equations that can be analyzed with
respect to their energy requirements. However, to explore how such
chemical systems might be engineered de novo, we also propose an extended
version based on enzyme-driven compartmentalized reactions. Finally,
we show how a purely DNA system, built upon the paradigm of DNA strand
displacement, can realize neuronal dynamics. Our analysis provides
a compelling blueprint for exploring autonomous learning in biological
settings, bringing us closer to realizing real synthetic biological
intelligence.
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Affiliation(s)
- Jakub Fil
- APT Group, School of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Neil Dalchau
- Microsoft Research, Cambridge CB1 2FB, United Kingdom
| | - Dominique Chu
- CEMS, School of Computing, University of Kent, Canterbury CT2 7NF, United Kingdom
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