Finally, we interrelate Loregic’s gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions , feed-forward loop motifs and global

In the past decade, an increasing number of experimental and computational studies have focused on analyzing links between RFs from various biological characteristics such as protein-protein interactions , sequence motifs in cis-regulatory modules of TF binding sites, co-associations of TFs in binding sites, and co-eXpressions of TF target genes [1,5—8].

By contrast, TFs can indirectly control gene expression without binding to regulatory sequence elements but rather connecting with other bound TFs through protein-protein interactions [2,24].

TFs can regulate target genes Without binding directly to regulatory regions by instead forming protein-protein interactions With already bound TFs [2].

This suggests that the motif-missing TF is only involved with the target gene indirectly—perhaps through a protein-protein interaction (specifically for this assessment, we define a TF binding motif are missing if we couldn’t find any matches in target promoter sequences for TF motifs with at least 80% Position Weight Matrix (PWM) similarity using matchPWM(.

By contrast, the AND-consistent triplet, (RF1 is USF2, RF2 is NFE2, T is NBPF1) has a USF2 motif but not an NFE2 motif in NBPF1’s promoter region, which is explained by reports that USF2 and NFE2 are connected through protein-protein interactions and that NFE2 regulates NBPF1 through indirect binding [2].

The other TF, NFE2 cooperates With USF2 in an AND logical relation Via protein-protein interaction .

Data were analyzed using a combination of graph theory and pattern recognition techniques that resolve data structure into networks that incorporate statistical relationships and protein-protein interaction data.

Thus, the complexity of kinase-sub-strate and other protein-protein interactions in tyrosine kinase signaling pathways is important to understand because these pathways govern the choice between differentiation and cancer.

By combining pattern recognition techniques with gene ontology (GO) and protein-protein interaction (PPI) data, we learned that clusters that contain interacting proteins are likely to indicate functional signaling pathways [34—40].

S1 Fig shows a network constructed using proteins identified in neuroblastoma phosphopro-teomic data as nodes, and protein-protein interaction (PPI) edges merged as described [34].

PPI databases are biased towards proteins best studied in the scientific literature [36—38] , and not all protein-protein interactions in PPI databases may occur in neuroblastoma cells.

Neuroblastoma protein-protein interaction (PPI) network.

Here, we used host-pathogen protein-protein interactions derived from yeast two-hybrid screens to study nine known B. mallei virulence factors and map out potential virulence mechanisms.

For the 498-protein set, we found 202 unique proteins that participated in 325 human protein-protein interactions .

mallei protein-protein interactions (PPIs) allowed us to identify three novel B. mallei ATCC 23344 virulence factors and show that they attenuated B. mallei virulence in mouse aerosol challenge experiments.

mallei protein-protein interactions (PPls) was created by merging human-B.