Network reconstruction based on proteomic data and prior knowledge of protein connectivity using graph theory.

Vassilis Stavrakas,Ioannis N Melas,Leonidas G Alexopoulos,Theodore Sakellaropoulos,Pablo Villoslada

doi:10.1371/journal.pone.0128411

Vassilis Stavrakas, Ioannis N Melas + Show 3 more

Open Access

https://doi.org/10.1371/journal.pone.0128411

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Abstract

Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights.

Highlights

Signaling pathways are of the utmost importance for understanding cellular function and predicting response to environmental perturbations [1,2,3,4,5,6,7]
The pathway reconstruction problem is formulated as the identification of optimal subsets of the prior knowledge network, conserving in the solution only the reactions that appear to be functional based on the data at hand
The proposed formulation requires a qualitative view of signal transduction, supporting only two discrete states indicating the variation of the activation state of signaling nodes (“1” for activation and “0” for unchanged state)

Summary

Introduction

Signaling pathways are of the utmost importance for understanding cellular function and predicting response to environmental perturbations [1,2,3,4,5,6,7]. Extensive collections of signaling pathways have been made available to online databases, obtained either from dedicated experiments, computational predictions or obtained manually from research articles Most of these interactions lack biological context (cell type, treatments etc.). Even with all these resources available, compiling a context specific network is a tedious and challenging task [8] On this front computational methodologies have been proposed that combine prior knowledge of protein interactions with experimental data in an attempt to uncover signaling pathways that appear to be functional in the interrogated cell/tissue type. The pathway reconstruction problem is formulated as the identification of optimal subsets of the prior knowledge network, conserving in the solution only the reactions that appear to be functional based on the data at hand

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