Analyzing and Modeling Large Biological Networks: Inferring Signal Transduction Pathways
Large scale two-hybrid screens have generated a wealth of information describing potential protein-protein intereactions (PPIs). When interacting proteins are associated with each other to generate networks, a map of the cell, picturing potential signaling pathways and interactive complexes is formed. PPI networks satisfy the small-world property and their degree distribution follow the power-law degree distribution. Recently, duplication based random graph models have been proposed to emulate the evolution of PPI networks and to satisfy these two graph theoretical properties. In this work, we show that the previously proposed model of Pastor-Satorras et al.(2003) does not generate a power-law degree distribution with exponential cutoff as claimed and the more restrictive model by Chung et al.(2003) cannot be interpreted unconditionally. It is possible to slightly modify these models to ensure that they generate a power-law degree distribution. However, even after this modification, the more general l-hop degree distribution achieved by these models, for l>1, are very different from that of the yeast proteome network. We address this problem by introducing a new network growth model taking into account the sequence similarity between pairs of proteins as well as their interactions. The new model captures the l-hop degree distribution of the yeast PPI network for all l>0, as well as the immediate degree distribution of the sequence similarity network. We further utilize the PPI networks to discover possible pathway segments. Discovering signal transduction pathways has been an arduous problem, even with the use of systematic genomic, proteomic and metabolomic technologies. The enormous amount of data and how to interpret and process this data becomes a challenging computational problem.In this work we present a new framework to identify signaling pathways in PPI networks. Our goal is to find biologically significant pathway segments in a given interaction network. First, we discover association rules based on known signal transduction pathways and their functional annotations. Given a pair of starting and ending proteins, our methodology returns candidate pathway segments between these two proteins. These candidate pathway segments are further filtered by their gene expression levels. In our study, we used the S. cerevisiae interaction network and microarray data, to successfully reconstruct signal transduction pathways in yeast.
School:Case Western Reserve University
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:computational biology data mining protein interaction networks evolution network growth models power law graphs signaling pathways signal transduction
Date of Publication:01/01/2007