To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors.

We extracted gene regulatory network data from the ENCODE leukemia cell line, K562, and gene and miRNA eXpression datasets for AML from TCGA.

At a high level, the gene regulatory network can be regarded as analogous to an electronic circuit insofar as both gene networks and electronic circuits have inputs and outputs related by certain rules.

While this model is not able to capture the very complex regulatory patterns that may be characterized by continuous models [12,13], it is computationally efficient, and it is comprehensive enough to meaningfully describe a large variety of regulatory networks on a genome-wide scale in multiple organisms.

By combining the activity of RFs and their respective targets on a genome-wide scale, a bigger picture emerges: the gene regulatory network .

FFLs have been found to be important motifs in regulatory networks , With many interacting by following logic operations [11].

Loregic takes as inputs two types of data: a regulatory network (defined by RFs and their target genes) and a binarized gene expression dataset across multiple samples.

Input gene regulatory network consisting of regulatory factors and their target genes;

Finally, Steps CF are repeated for all triplets in the regulatory network , and all logic-gate-consistent triplets are identified.

Mean intensity values for each region/ network served as nodes in co-activation analyses.

By saving the average intensity values for each region/ network from each MCMC iteration, we were able to estimate the co-activation intensity as the correlation between average intensity values for each pair of regions.

Average co-activation Within and between each region/ network grouping, for comparison to global network efficiency values based on path length in Fig.