ArticlePLoS computational biology2017
Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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.
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.
Who cites it
27 citing papers in PubMed, 55 citations in OpenAlex.
- Article
- Architecture and regulatory functions of c-di-GMP signaling in classical Bordetella species.FEMS microbiology reviews · 2026Review
- Article
- Cyclic Diguanylate in the Wild: Roles During Plant and Animal Colonization.Annual review of microbiology · 2024Review
- Antifungal heteroresistance causes prophylaxis failure and facilitates breakthrough Candida parapsilosis infections.Nature medicine · 2024Article
- Revisiting the evolution of bow-tie architecture in signaling networks.NPJ systems biology and applications · 2024Article
- Article
- Pleiotropic hubs drive bacterial surface competition through parallel changes in colony composition and expansion.PLoS biology · 2023Article
- Emergence of Exopolysaccharides Overproducers Is Linked to Environmental Spatial Structure via Redox State.mSphere · 2023Article
- Genome-Based Analysis of Virulence Factors and Biofilm Formation in NovelMicroorganisms · 2022Article
- Article
- Identification of Cyclic-di-GMP-Modulating Protein Residues by Bidirectionally Evolving a Social Behavior in Pseudomonas fluorescens.mSystems · 2022Article
- Mathematical models to study the biology of pathogens and the infectious diseases they cause.iScience · 2022Review
- Evolutionary Divergence of the Wsp Signal Transduction Systems in Beta- and Gammaproteobacteria.Applied and environmental microbiology · 2021Article
- Estimation of pathogenic potential of an environmental Pseudomonas aeruginosa isolate using comparative genomics.Scientific reports · 2021Article
- One gene, multiple ecological strategies: A biofilm regulator is a capacitor for sustainable diversity.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Application of artificial intelligence to theCanada communicable disease report = Releve des maladies transmissibles au Canada · 2020Article
- Mechanomicrobiology: how bacteria sense and respond to forces.Nature reviews. Microbiology · 2020Review
- Systems-level analysis of NalD mutation, a recurrent driver of rapid drug resistance in acute Pseudomonas aeruginosa infection.PLoS computational biology · 2019Article
- Causes and Biophysical Consequences of Cellulose Production by Pseudomonas fluorescens SBW25 at the Air-Liquid Interface.Journal of bacteriology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world-the input stimuli-into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility-the output phenotypes. How does the 'uninformed' process of evolution produce a network with the right input/output association and enable bacteria to make the right choice? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors. Our model clarifies the elusive function of the ubiquitous c-di-GMP network, a key regulator of bacterial social traits associated with virulence. More broadly, the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.