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

Gunnar Boldhaus, Florian Greil, Konstantin Klemm

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)17-25
Number of pages9
JournalTheory in Biosciences
Volume132
Issue number1
DOIs
Publication statusPublished - Jan 1 2013

    Fingerprint

Keywords

  • Boolean network
  • Knock-out
  • Network centrality
  • Prediction
  • Synthetic lethality

ASJC Scopus subject areas

  • Statistics and Probability
  • Ecology, Evolution, Behavior and Systematics
  • Applied Mathematics

Cite this