Abstract | 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 |
Introduction | 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]. |
Introduction | 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]. |
Loregic applications for other regulatory features | TFs can regulate target genes Without binding directly to regulatory regions by instead forming protein-protein interactions With already bound TFs [2]. |
Loregic applications for other regulatory features | 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(. |
Loregic applications for other regulatory features | 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]. |
Supporting Information | The other TF, NFE2 cooperates With USF2 in an AND logical relation Via protein-protein interaction . |
Abstract | 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. |
Introduction | 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. |
Introduction | 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]. |
Neuroblastoma Phosphoproteomic Network | 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]. |
Neuroblastoma Phosphoproteomic Network | 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. |
Supporting Information | Neuroblastoma protein-protein interaction (PPI) network. |