Evidence map›Paper›PMID 35790733›Full record

ArticleNature communications2022

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 and 5 more

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers.

0numbers the graph read from it
0cells of the map it votes in
65citing 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

65 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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  8. Improving cell-free metabolism through direct integration of artificial respiratory chains.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Multi-gene Co-expression systems inSynthetic and systems biotechnology · 2026
    Review
  16. Article
  17. Article
  18. A Guide to Bayesian Optimization in Bioprocess Engineering.Biotechnology and bioengineering · 2026
    Review
  19. Article
  20. Article

5 more citing papers are in PubMed but not listed here.

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

15 authors.

Amir Pandi *Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany. amir.pandi@mpi-marburg.mpg.de.ORCID http://orcid.org/0000-0002-5204-756X
Christoph Diehl *Department of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Ali Yazdizadeh KharraziDataChef, Amsterdam, The Netherlands.
Scott A ScholzDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.ORCID http://orcid.org/0000-0001-9168-9285
Elizaveta BobkovaDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Léon FaureMicalis Institute, INRAE, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France.
Maren NattermannDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
David AdamDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Nils ChapinDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Yeganeh ForoughijabbariDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Charles MoritzDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Nicole PacziaCore Facility for Metabolomics and Small Molecule Mass Spectrometry, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Niña Socorro CortinaDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany.
Jean-Loup FaulonMicalis Institute, INRAE, AgroParisTech, University of Paris-Saclay, Jouy-en-Josas, France.
Tobias J ErbDepartment of Biochemistry & Synthetic Metabolism, Max Planck Institute for Terrestrial Microbiology, Marburg, Germany. toerb@mpi-marburg.mpg.de.ORCID http://orcid.org/0000-0003-3685-0894

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 CO

Indexed as

Carbon DioxideMetabolic Networks and PathwaysGene Regulatory NetworksSupervised Machine LearningWorkflowCarbon Dioxide

Identifiers

PMID35790733
PMCPMC9256728

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Registered trials

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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.