Switch-like Transitions Insulate Network Motifs to Modularize Biological Networks

Oguzhan Atay,Andreas Doncic,Jan M Skotheim

doi:10.1016/j.cels.2016.06.010

Abstract

Cellular decisions are made by complex networks that are difficult to analyze. Although it is common to analyze smaller sub-networks known as network motifs, it is unclear whether this is valid, because these motifs are embedded in complex larger networks. Here, we address the general question of modularity by examining the S.cerevisiae pheromone response. We demonstrate that the feedforward motif controlling the cell-cycle inhibitor Far1 is insulated from cell-cycle dynamics by the positive feedback switch that drives reentry to the cell cycle. Before cells switch on positive feedback, the feedforward motif model predicts the behavior of the larger network. Conversely, after the switch, the feedforward motif is dismantled and has no discernable effect on the cell cycle. When insulation is broken, the feedforward motif no longer predicts network behavior. This work illustrates how, despite the interconnectivity of networks, the activity of motifs can be insulated by switches that generate well-defined cellular states.

Highlights

In the past few decades, a large body of work has identified many components of signaling networks, ordered them in pathways, and determined many of their biochemical interactions (Gerhart, 1999; Perrimon et al, 2012)
We show that modularity of the feedforward motif results from the presence of multiple positive feedbacks that convert an analog input into an ON/OFF digital output
The Feedforward Motif Regulating Far1 Is Insulated from the Cell Cycle during Arrest That the feedforward motif analysis predicts cellular behavior suggests a modular network structure

Summary

Introduction

In the past few decades, a large body of work has identified many components of signaling networks, ordered them in pathways, and determined many of their biochemical interactions (Gerhart, 1999; Perrimon et al, 2012). It has remained difficult to use this molecular knowledge to accurately predict protein activities and cell behavior. This is primarily because there are too many protein interactions for which the kinetic parameters are not known, and many of these are nonlinear (Boone et al, 2007; Yosef and Regev, 2011). Despite the vast increase in our knowledge of molecular interactions, how cells process information (i.e., how biological networks integrate dynamic signals to determine cellular responses) remains poorly understood. It is very difficult to analyze a signaling network in its entirety, separate timescales of biological interactions often allow complex networks to be broken into sub-networks that can be analyzed independently (Alon, 2006). Separation of timescales can enable the separation of complex networks into smaller sub-networks

Results

Discussion

Conclusion