Bacterial gene regulatory neural network as a biocomputing library of mathematical solvers
Adrian Ratwatte; Samitha Somathilaka; Ngoc Dan Thanh Cao; Xu Li; Sasitharan Balasubramaniam · npj Systems Biology and Applications · 2026 · Transcriptomic data analysis and gene-network computational modelling
Original publication · Original publication · Original publication
Recorded summary
A search procedure identifies bacterial regulatory subnetworks whose expression responses match several finite mathematical tasks. One output-gene pattern approximates Fibonacci numbers across a small set of chemically encoded conditions.
Recorded limitations
Subnetworks are selected for agreement with the target pattern. The finite input set and computational stability analyses do not establish unrestricted arithmetic, natural selection for Fibonacci computation, or performance on unseen tasks.
Evidence and provenance
Chapter Trust Factor uses a minimum of three distinct qualifying studies. Publication records retain their own evidence and assessment.
Study design and assessment
- design
- Transcriptomic data analysis and gene-network computational modelling
- assessment
- is Randomized Controlled Trial
- false
- blinding
- Not applicable to a clinical-trial grade
- basis
- Transcriptomic data analysis and gene-network computational modelling. No human randomized controlled trial is reported.
- sources
https://www.nature.com/articles/s41540-026-00733-z
- category
- peer-reviewed
- peer Reviewed
- true
- trust Factor
- score
- 1
- scope
- Paper
- status
- assessed
- randomized Controlled Trial
- false
- blinding
- not-confirmed
- peer Reviewed
- true
- basis
- Transcriptomic data analysis and gene-network computational modelling. No human randomized controlled trial is reported.
- sources
https://pubmed.ncbi.nlm.nih.gov/42120901/ · https://www.nature.com/articles/s41540-026-00733-z
- review Scope
- Existing publication-specific assessments and bibliographic indexing; no blanket full-text appraisal.
- assessed At
- 2026-10-27
- jadad
- status
- not-applicable
- score
- Not recorded
- reason
- Not a human randomized controlled trial.
- evidence
- evidence Type
- Transcriptomic data analysis and gene-network computational modelling
- evidence Class
- biological_data_computational_model
- measurement
- E. coli K-12 expression data under chemically encoded conditions; network search and perturbation models
- measured Genes
E. coli b4056 (selected Fibonacci-task output; broader transcriptome also measured)
- pathways
- None recorded
- relationship Note
- Background literature only. This paper does not test or validate an OXTR–Fibonacci relationship.
- clinical Evidence
- false
- review Depth
- Abstract and selected source passages: Abstract; Introduction; Results, Fibonacci-number task.
- full Paper Appraisal
- Not completed; review depth states the material assessed.
Topics and recorded topic scores
- key
- general:fibonacci-molecular-computation
- title
- Molecular enumeration and biological computation
- collection
- general
- trust Score
- 1
Citation snapshot and identifiers
- id
- nature-fibonacci-s41540-026-00733-z
- doi
- 10.1038/s41540-026-00733-z
- pmid
- 42120901
- citation Snapshot
- count
- 0
- source
- Crossref cited-by count
- retrieved At
- 2026-10-24
- url
- https://api.crossref.org/works/10.1038/s41540-026-00733-z
- field
- is-referenced-by-count
- source Indexed At
- 2026-07-21T23:02:57Z
- coverage Note
- Count reflects Crossref-deposited references and can differ from publisher or other database totals. It is not a quality or clinical-evidence score.
- citations By Collection
- general
- 0
Complete approved library record
- id
- nature-fibonacci-s41540-026-00733-z
- aliases
- None recorded
- title
- Bacterial gene regulatory neural network as a biocomputing library of mathematical solvers
- journal
- npj Systems Biology and Applications
- publisher Family
- nature
- publisher Family Label
- Nature Portfolio
- neurotransmitters
- None recorded
- year
- 2026
- authors
- Adrian Ratwatte; Samitha Somathilaka; Ngoc Dan Thanh Cao; Xu Li; Sasitharan Balasubramaniam
- design
- Transcriptomic data analysis and gene-network computational modelling
- kind
- Journal article
- identity Conflict
- Not recorded
- doi
- 10.1038/s41540-026-00733-z
- pmid
- 42120901
- urls
https://www.nature.com/articles/s41540-026-00733-z · https://doi.org/10.1038/s41540-026-00733-z · https://pubmed.ncbi.nlm.nih.gov/42120901/
- collections
general
- categories
Fibonacci mathematics · Molecular computation
- targets
- None recorded
- priority
- 1
- quotes
- None recorded
- summary
- A search procedure identifies bacterial regulatory subnetworks whose expression responses match several finite mathematical tasks. One output-gene pattern approximates Fibonacci numbers across a small set of chemically encoded conditions.
- limitation
- Subnetworks are selected for agreement with the target pattern. The finite input set and computational stability analyses do not establish unrestricted arithmetic, natural selection for Fibonacci computation, or performance on unseen tasks.
- citation Count
- 0
- citation Snapshot
- count
- 0
- source
- Crossref cited-by count
- retrieved At
- 2026-10-24
- url
- https://api.crossref.org/works/10.1038/s41540-026-00733-z
- field
- is-referenced-by-count
- source Indexed At
- 2026-07-21T23:02:57Z
- coverage Note
- Count reflects Crossref-deposited references and can differ from publisher or other database totals. It is not a quality or clinical-evidence score.
- study Design Assessment
- is Randomized Controlled Trial
- false
- blinding
- Not applicable to a clinical-trial grade
- basis
- Transcriptomic data analysis and gene-network computational modelling. No human randomized controlled trial is reported.
- sources
https://www.nature.com/articles/s41540-026-00733-z
- category
- peer-reviewed
- peer Reviewed
- true
- paper Trust Assessment
- score
- 1
- scope
- Paper
- status
- assessed
- randomized Controlled Trial
- false
- blinding
- not-confirmed
- peer Reviewed
- true
- basis
- Transcriptomic data analysis and gene-network computational modelling. No human randomized controlled trial is reported.
- sources
https://pubmed.ncbi.nlm.nih.gov/42120901/ · https://www.nature.com/articles/s41540-026-00733-z
- review Scope
- Existing publication-specific assessments and bibliographic indexing; no blanket full-text appraisal.
- assessed At
- 2026-10-27
- citations By Collection
- general
- 0
- topics
- key
- general:fibonacci-molecular-computation
- title
- Molecular enumeration and biological computation
- collection
- general
- trust Score
- 1
- journal Key
- npjsystemsbiologyandapplications
- journal Label
- npj Systems Biology and Applications
- chapter Entries
- id
- general-nature-fibonacci-s41540-026-00733-z
- title
- Bacterial gene regulatory neural network as a biocomputing library of mathematical solvers
- journal
- npj Systems Biology and Applications
- year
- 2026
- authors
- Adrian Ratwatte; Samitha Somathilaka; Ngoc Dan Thanh Cao; Xu Li; Sasitharan Balasubramaniam
- doi
- 10.1038/s41540-026-00733-z
- pmid
- 42120901
- urls
https://www.nature.com/articles/s41540-026-00733-z · https://doi.org/10.1038/s41540-026-00733-z · https://pubmed.ncbi.nlm.nih.gov/42120901/
- design
- Transcriptomic data analysis and gene-network computational modelling
- summary
- A search procedure identifies bacterial regulatory subnetworks whose expression responses match several finite mathematical tasks. One output-gene pattern approximates Fibonacci numbers across a small set of chemically encoded conditions.
- limitation
- Subnetworks are selected for agreement with the target pattern. The finite input set and computational stability analyses do not establish unrestricted arithmetic, natural selection for Fibonacci computation, or performance on unseen tasks.
- citation Count
- 0
- citation Snapshot
- count
- 0
- source
- Crossref cited-by count
- retrieved At
- 2026-10-24
- url
- https://api.crossref.org/works/10.1038/s41540-026-00733-z
- field
- is-referenced-by-count
- source Indexed At
- 2026-07-21T23:02:57Z
- coverage Note
- Count reflects Crossref-deposited references and can differ from publisher or other database totals. It is not a quality or clinical-evidence score.
- categories
Fibonacci mathematics · Molecular computation
- targets
- None recorded
- evidence
- evidence Type
- Transcriptomic data analysis and gene-network computational modelling
- evidence Class
- biological_data_computational_model
- measurement
- E. coli K-12 expression data under chemically encoded conditions; network search and perturbation models
- measured Genes
E. coli b4056 (selected Fibonacci-task output; broader transcriptome also measured)
- pathways
- None recorded
- relationship Note
- Background literature only. This paper does not test or validate an OXTR–Fibonacci relationship.
- clinical Evidence
- false
- review Depth
- Abstract and selected source passages: Abstract; Introduction; Results, Fibonacci-number task.
- full Paper Appraisal
- Not completed; review depth states the material assessed.
- record Id
- nature-fibonacci-s41540-026-00733-z
- quotes
- None recorded
- source Pages
- None recorded
- paper Trust Assessment
- score
- 1
- scope
- Paper
- status
- assessed
- randomized Controlled Trial
- false
- blinding
- not-confirmed
- peer Reviewed
- true
- basis
- Transcriptomic data analysis and gene-network computational modelling. No human randomized controlled trial is reported.
- sources
https://pubmed.ncbi.nlm.nih.gov/42120901/ · https://www.nature.com/articles/s41540-026-00733-z
- review Scope
- Existing publication-specific assessments and bibliographic indexing; no blanket full-text appraisal.
- assessed At
- 2026-10-27
- study Design Assessment
- is Randomized Controlled Trial
- false
- blinding
- Not applicable to a clinical-trial grade
- basis
- Transcriptomic data analysis and gene-network computational modelling. No human randomized controlled trial is reported.
- sources
https://www.nature.com/articles/s41540-026-00733-z
- category
- peer-reviewed
- peer Reviewed
- true
- collection
- general
- chapter Number
- 9
- topic Id
- fibonacci-molecular-computation
- topic Title
- Molecular enumeration and biological computation
- trust Score
- 1
- evidence
- evidence Type
- Transcriptomic data analysis and gene-network computational modelling
- evidence Class
- biological_data_computational_model
- measurement
- E. coli K-12 expression data under chemically encoded conditions; network search and perturbation models
- measured Genes
E. coli b4056 (selected Fibonacci-task output; broader transcriptome also measured)
- pathways
- None recorded
- relationship Note
- Background literature only. This paper does not test or validate an OXTR–Fibonacci relationship.
- clinical Evidence
- false
- review Depth
- Abstract and selected source passages: Abstract; Introduction; Results, Fibonacci-number task.
- full Paper Appraisal
- Not completed; review depth states the material assessed.
- jadad
- status
- not-applicable
- score
- Not recorded
- reason
- Not a human randomized controlled trial.
- condition Evidence
- None recorded
- conditions
- None recorded
- topic Trust Scores
1
- full Text
- status
- not-indexed
- retrieval Available
- false