Prediction of lethal and synthetically lethal knock-outs in regulatory networks

TL;DR

This study explores how simple network measures like out-degree and out-overlap can predict lethal and synthetic lethality effects of node knock-outs in regulatory networks, using minimal information about network structure.

Contribution

It demonstrates that basic centrality measures can effectively forecast node lethality and synthetic lethality without detailed functional knowledge.

Findings

Out-degree predicts single-node lethality effectively.

Out-overlap predicts synthetic lethality between node pairs.

Simple local measures perform nearly as well as complex predictors.

Abstract

The complex interactions involved in regulation of a cell's function are captured by its interaction graph. More often than not, detailed knowledge about enhancing or suppressive regulatory influences and cooperative effects is lacking and merely the presence or absence of directed interactions is known. Here we investigate to which extent such reduced information allows to forecast the effect of a knock-out or a combination of knock-outs. Specifically we ask in how far the lethality of eliminating nodes may be predicted by their network centrality, such as degree and betweenness, without knowing the function of the system. The function is taken as the ability to reproduce a fixed point under a discrete Boolean dynamics. We investigate two types of stochastically generated networks: fully random networks and structures grown with a mechanism of node duplication and subsequent divergence of interactions. On all networks we find that the out-degree is a good predictor of the lethality of a single node knock-out. For knock-outs of node pairs, the fraction of successors shared between the two knocked-out nodes (out-overlap) is a good predictor of synthetic lethality. Out-degree and out-overlap are locally defined and computationally simple centrality measures that provide a predictive power close to the optimal predictor.