Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network Journal Article


Authors: Yan, J.; Deforet, M.; Boyle, K. E.; Rahman, R.; Liang, R.; Okegbe, C.; Dietrich, L. E. P.; Qiu, W.; Xavier, J. B.
Article Title: Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network
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.
Keywords: signal transduction; phenotype; metabolism; biological model; biology; computational biology; models, biological; cell motion; physiology; cell movement; pseudomonas aeruginosa; cyclic gmp; biofilms; machine learning; biofilm; analogs and derivatives; bis(3',5')-cyclic diguanylic acid
Journal Title: PLoS Computational Biology
Volume: 13
Issue: 8
ISSN: 1553-7358
Publisher: Public Library of Science  
Date Published: 2017-08-02
Start Page: e1005677
Language: English
DOI: 10.1371/journal.pcbi.1005677
PUBMED: 28767643
PROVIDER: scopus
PMCID: PMC5555705
DOI/URL:
Notes: Article -- Export Date: 5 September 2017 -- Source: Scopus
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MSK Authors
  1. Joao Debivar Xavier
    78 Xavier
  2. Kerry Eileen Boyle
    7 Boyle
  3. Jinyuan Yan
    6 Yan