Elucidation of time-dependent systems biology cell response patterns with time course network enrichment

TL;DR

This paper introduces TiCoNE, a novel method that integrates time series gene expression data with network analysis to identify dynamic biological response patterns, demonstrated on viral infection data.

Contribution

TiCoNE is the first method to combine human-augmented clustering with network enrichment for time-dependent systems biology analysis.

Findings

Identified distinct temporal response profiles in viral-infected lung cells.

Demonstrated the method's ability to compare temporal patterns across conditions.

Provided an accessible online tool and Cytoscape app for researchers.

Abstract

Advances in OMICS technologies emerged both massive expression data sets and huge networks modelling the molecular interplay of genes, RNAs, proteins and metabolites. Network enrichment methods combine these two data types to extract subnetwork responses from case/control setups. However, no methods exist to integrate time series data with networks, thus preventing the identification of time-dependent systems biology responses. We close this gap with Time Course Network Enrichment (TiCoNE). It combines a new kind of human-augmented clustering with a novel approach to network enrichment. It finds temporal expression prototypes that are mapped to a network and investigated for enriched prototype pairs interacting more often than expected by chance. Such patterns of temporal subnetwork co-enrichment can be compared between different conditions. With TiCoNE, we identified the first distinguishing temporal systems biology profiles in time series gene expression data of human lung cells after infection with Influenza and Rhino virus. TiCoNE is available online (https://ticone.compbio.sdu.dk) and as Cytoscape app in the Cytoscape App Store (http://apps.cytoscape.org/).