Network motifs in integrated cellular networks of transcription–regulation and protein–protein interaction

TLDR

Genes and proteins form molecular circuitry that processes information, and prior work has examined transcription regulation or protein–protein interactions separately. The study aims to identify composite network motifs that combine transcription regulation and protein–protein interactions and recur more frequently than expected by chance. Algorithms were developed to detect motifs in multi‑type interaction networks and applied to an integrated Saccharomyces cerevisiae dataset of protein–protein interactions and transcription regulation. The analysis uncovered a two‑protein mixed‑feedback loop, five distinct three‑protein motifs with coregulation and complex formation, numerous four‑protein motifs largely composed of smaller motifs, and established a basic framework for detecting such building blocks.

Abstract

Genes and proteins generate molecular circuitry that enables the cell to process information and respond to stimuli. A major challenge is to identify characteristic patterns in this network of interactions that may shed light on basic cellular mechanisms. Previous studies have analyzed aspects of this network, concentrating on either transcription–regulation or protein–protein interactions. Here we search for composite network motifs: characteristic network patterns consisting of both transcription–regulation and protein–protein interactions that recur significantly more often than in random networks. To this end we developed algorithms for detecting motifs in networks with two or more types of interactions and applied them to an integrated data set of protein–protein interactions and transcription regulation in Saccharomyces cerevisiae . We found a two-protein mixed-feedback loop motif, five types of three-protein motifs exhibiting coregulation and complex formation, and many motifs involving four proteins. Virtually all four-protein motifs consisted of combinations of smaller motifs. This study presents a basic framework for detecting the building blocks of networks with multiple types of interactions.