Fusion of gene regulatory and protein interaction networks using skip-chain models

Abstract

Inference of Gene Regulatory Networks (GRN) is important in understanding signal transduction pathways. This involves predicting the correct sequence of interactions and identifying all interacting genes. Using only gene expression data is insufficient, so additional sources of data like protein-protein interaction network (PPIN) are required. In this paper, we model time delayed interactions using a skip-chain model which finds missing edges between non-consecutive time points based on PPIN. Highest Viterbi approximation is used to select skip-edges. The k-skip validation model checks for k missing genes between a predicted interaction, giving us advantages of validation as well as expansion of GRN. The method is demonstrated on a cell-division cycle data of S.cerevisiae (yeast). Comparison of the present method, with a previous approach of modeling PPIN by using a Gibbs prior are given. © 2008 Springer Berlin Heidelberg.