Evidence map›Paper›PMID 34035266›Full record

ArticleNature communications2021

Synthetic neural-like computing in microbial consortia for pattern recognition.

Ximing Li, Luna Rizik, Valeriia Kravchik, Maria Khoury, Netanel Korin, Ramez Daniel

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. ACS synthetic biology · 2025
    Article
  5. Review
  6. Article
  7. Review
  8. Article
  9. Review
  10. SemiSynBio: A new era for neuromorphic computing.Synthetic and systems biotechnology · 2024
    Review
  11. Article
  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Article
  19. Article
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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

6 authors.

Ximing LiDepartment of Biomedical Engineering Technion-Israel Institute of Technology, Technion City, Haifa, Israel.ORCID 0000-0002-5157-697X
Luna RizikDepartment of Biomedical Engineering Technion-Israel Institute of Technology, Technion City, Haifa, Israel.ORCID 0000-0002-8733-3646
Valeriia KravchikDepartment of Biomedical Engineering Technion-Israel Institute of Technology, Technion City, Haifa, Israel.
Maria KhouryDepartment of Biomedical Engineering Technion-Israel Institute of Technology, Technion City, Haifa, Israel.
Netanel KorinDepartment of Biomedical Engineering Technion-Israel Institute of Technology, Technion City, Haifa, Israel.ORCID 0000-0001-7244-889X
Ramez DanielDepartment of Biomedical Engineering Technion-Israel Institute of Technology, Technion City, Haifa, Israel. ramizda@bm.technion.ac.il.ORCID 0000-0001-8886-3668

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Complex biological systems in nature comprise cells that act collectively to solve sophisticated tasks. Synthetic biological systems, in contrast, are designed for specific tasks, following computational principles including logic gates and analog design. Yet such approaches cannot be easily adapted for multiple tasks in biological contexts. Alternatively, artificial neural networks, comprised of flexible interactions for computation, support adaptive designs and are adopted for diverse applications. Here, motivated by the structural similarity between artificial neural networks and cellular networks, we implement neural-like computing in bacteria consortia for recognizing patterns. Specifically, receiver bacteria collectively interact with sender bacteria for decision-making through quorum sensing. Input patterns formed by chemical inducers activate senders to produce signaling molecules at varying levels. These levels, which act as weights, are programmed by tuning the sender promoter strength Furthermore, a gradient descent based algorithm that enables weights optimization was developed. Weights were experimentally examined for recognizing 3 × 3-bit pattern.

Indexed as

Computers, MolecularEscherichia coliMicrobial ConsortiaNeural Networks, ComputerPattern Recognition, AutomatedQuorum SensingSynthetic Biology

Identifiers

PMID34035266
PMCPMC8149857

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.