{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T15:37:21Z","timestamp":1784129841823,"version":"3.55.0"},"reference-count":95,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T00:00:00Z","timestamp":1701302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p><jats:italic>Pseudomonas aeruginosa<\/jats:italic> infections are one of the leading causes of death in immunocompromised patients with cystic fibrosis, diabetes, and lung diseases such as pneumonia and bronchiectasis. Furthermore, <jats:italic>P. aeruginosa<\/jats:italic> is one of the main multidrug-resistant bacteria responsible for nosocomial infections worldwide, including the multidrug-resistant CCBH4851 strain isolated in Brazil.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>One way to analyze their dynamic cellular behavior is through computational modeling of the gene regulatory network, which represents interactions between regulatory genes and their targets. For this purpose, Boolean models are important predictive tools to analyze these interactions. They are one of the most commonly used methods for studying complex dynamic behavior in biological systems.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results and discussion<\/jats:title><jats:p>Therefore, this research consists of building a Boolean model of the gene regulatory network of <jats:italic>P. aeruginosa<\/jats:italic> CCBH4851 using data from RNA-seq experiments. Next, the basins of attraction are estimated, as these regions and the transitions between them can help identify the attractors, representing long-term behavior in the Boolean model. The essential genes of the basins were associated with the phenotypes of the bacteria for two conditions: biofilm formation and polymyxin B treatment. Overall, the Boolean model and the analysis method proposed in this work can identify promising control actions and indicate potential therapeutic targets, which can help pinpoint new drugs and intervention strategies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmicb.2023.1274740","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T09:03:07Z","timestamp":1701334987000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851"],"prefix":"10.3389","volume":"14","author":[{"given":"M\u00e1rcia da Silva","family":"Chagas","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo","family":"Trindade dos Santos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcio","family":"Argollo de Menezes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabricio Alves Barbosa","family":"da Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"B1","doi-asserted-by":"crossref","DOI":"10.1038\/s41540-022-00242-9","article-title":"An expanded whole-cell model of E. coli links cellular physiology with mechanisms of growth rate control.","volume":"8","author":"Ahn-Horst","year":"2022","journal-title":"NPJ Syst. 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