Multi-omic Network Regression: Methodology, Tool and Case Study

TL;DR

This paper introduces a novel multi-omic network regression methodology combining metabolic modeling and Cox regression, demonstrated on Helicobacter Pylori, with a new software tool for complex network analysis.

Contribution

It presents the first integration of metabolic modeling with networked Cox regression and provides a versatile, publicly available tool for analyzing complex biological networks.

Findings

Reconstruction of data was more successful than noise.

The methodology effectively analyzed multi-omic data.

The approach is generalizable to other complex network studies.

Abstract

The analysis of biological networks is characterized by the definition of precise linear constraints used to cumulatively reduce the solution space of the computed states of a multi-omic (for instance metabolic, transcriptomic and proteomic) model. In this paper, we attempt, for the first time, to combine metabolic modelling and networked Cox regression, using the metabolic model of the bacterium Helicobacter Pylori. This enables a platform both for quantitative analysis of networked regression, but also testing the findings from network regression (a list of significant vectors and their networked relationships) on in vivo transcriptomic data. Data generated from the model, using flux balance analysis to construct a Pareto front, specifically, a trade-off of Oxygen exchange and growth rate and a trade-off of Carbon Dioxide exchange and growth rate, is analysed and then the model is used to quantify the success of the analysis. It was found that using the analysis, reconstruction of the initial data was considerably more successful than a pure noise alternative. Our methodological approach is quite general and it could be of interest for the wider community of complex networks researchers; it is implemented in a software tool, MoNeRe, which is freely available through the Github platform.