An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851

Boolean model of the gene regulatory network of Pseudomonas aeruginosa CCBH4851

“…This Figure shows that this biofilm GRN is highly interconnected, with only three regulatory genes and their targets disconnected from the larger component. According to Chagas et al (2022), this variability in the number of connected 10.3389/fmicb.2023.1274740 components is most likely linked to the availability of biological information used for the interaction reconstruction. This illustrates how biofilm formation is a complex phenomenon emerging from the interactions of numerous genes.…”

“…The CCBH-2022 GRN ( Chagas et al, 2022 ) is the largest and most comprehensive version of a P. aeruginosa GRN published to date, with edges classified as activation, repression, dual, and unknown, as described in dedicated biological databases and scientific literature. The Boolean model of CCBH-2022 proposed in this work is a PBN due to the uncertainty of dual and unknown edges, with uniform probability values ( P = 0.5) for expressed function (Boolean value 1) and non-expressed function (Boolean value 0) ( Shmulevich et al, 2002 ).…”

“…CCBH-2022 consists of 5,428 regulatory interactions among 3,139 gene products, of which 212 were identified as regulatory genes and 2,927 as target genes. It represents approximately 48% of the P. aeruginosa CCBH4851 genome and includes 3,821 positive regulation interactions, 643 negative regulation interactions, 19 double regulation interactions, and 945 unknown interactions ( Chagas et al, 2022 ).…”

“…A more comprehensive understanding of P. aeruginosa behavior can be obtained by analyzing the dynamics of its gene regulatory network (GRN) due to the predicted gene expression patterns ( Chagas et al, 2022 ). In recent years, computational modeling methods have been employed to simulate complex biological processes influenced by numerous factors, including the construction of biological networks and analysis of gene, metabolic, signal transduction pathways, and/or protein interactions ( Das et al, 2010 ; Tatarinova and Nikolsky, 2017 ).…”

“…The state space of Boolean networks grows exponentially with the number of nodes. In this work, due to the size and complexity of the GRN of P. aeruginosa CCBH 4851 ( Chagas et al, 2022 ), there was a need to reduce the GRN into a core sub-network. Kauffman et al (2003) describe that the definition of the core sub-network for attractor calculation should consider all nodes with at least one outgoing edge, which can change the network state from one timestep t to the next.…”