Asymptotically unbiased and consistent estimation of motif counts in biological networks from noisy subnetwork data

Abstract

Small over-represented motifs in biological networks often form essential functional units of biological processes. A natural question is to gauge whether a motif occurs abundantly or rarely in a biological network. Given that current high-throughput biotechnologies are only able to interrogate a portion of the entire biological network with non-negligible errors, in this thesis we develop a powerful method to estimate motif counts in the entire network from noisy and incomplete subnetwork data. Our theoretical analysis and extensive simulation validation show that the proposed estimators are asymptotically unbiased and consistent. When applying them to eukaryotic interactomes and gene regulatory networks, we discover several important features including the significant enrichment of functional motifs, the linear correlation between motif counts, the association between motif counts and cell¿s functions, etc. Our method does not depend on the underlying network model and can be easily extended to any complex network in the real world