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PLoS Comput Biol. 2015 Apr 17;11(4):e1004132. doi: 10.1371/journal.pcbi.1004132. eCollection 2015 Apr.

Loregic: a method to characterize the cooperative logic of regulatory factors.

Author information

1
Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America.
2
Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, United States of America.
3
School of Medicine, Yale University, New Haven, Connecticut, United States of America.
4
Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, United States of America; Department of Computer Science, Yale University, New Haven, Connecticut, United States of America.

Abstract

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet's observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic's gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

PMID:
25884877
PMCID:
PMC4401777
DOI:
10.1371/journal.pcbi.1004132
[Indexed for MEDLINE]
Free PMC Article

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