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https://www.selleckchem.com/products/k03861.html Pseudomonas aeruginosa is an opportunistic pathogen with a large repertoire of virulence factors that allow it to cause acute and chronic infections. Treatment of P. aeruginosa infections often fail due to its antibiotic resistance mechanisms, thus novel strategies aim at targeting virulence factors instead of growth-related features. Although the elements of the virulence networks of P. aeruginosa have been identified, how they interact and influence the overall virulence regulation is unclear. In this study, we reconstructed the signaling and transcriptional regulatory networks of 12 acute and 8 chronic virulence factors, and the 4 quorum sensing systems of P. aeruginosa. Using Boolean modelling, we showed that the static interactions and the time when they take place are important features in the quorum sensing network. We also found that the virulence factors of the acute networks are under strict repression or non-strict activation, while those of most of the chronic networks are under repression. In conclusion, Boolean modelling provides a system-level view of the P. aeruginosa virulence and quorum sensing networks to gain new insights into the various mechanisms that support its pathogenicity. Thus, we suggest that Boolean modelling could be used to guide the design of new treatments against P. aeruginosa.Molecular data systems have the potential to store information at dramatically higher density than existing electronic media. Some of the first experimental demonstrations of this idea have used DNA, but nature also uses a wide diversity of smaller non-polymeric molecules to preserve, process, and transmit information. In this paper, we present a general framework for quantifying chemical memory, which is not limited to polymers and extends to mixtures of molecules of all types. We show that the theoretical limit for molecular information is two orders of magnitude denser by mass than DNA, although this comes

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