Mapping biological process relationships and disease perturbations within a pathway network

Stoney, R., Robertson, D. L. , Nenadic, G. and Schwartz, J.-M. (2018) Mapping biological process relationships and disease perturbations within a pathway network. npj Systems Biology and Applications, 4, 22. (doi: 10.1038/s41540-018-0055-2) (PMID:29900005) (PMCID:PMC5995814)

[img]
Preview
Text
203703.pdf - Published Version
Available under License Creative Commons Attribution.

2MB

Abstract

Molecular interaction networks are routinely used to map the organization of cellular function. Edges represent interactions between genes, proteins, or metabolites. However, in living cells, molecular interactions are dynamic, necessitating context-dependent models. Contextual information can be integrated into molecular interaction networks through the inclusion of additional molecular data, but there are concerns about completeness and relevance of this data. We developed an approach for representing the organization of human cellular processes using pathways as the nodes in a network. Pathways represent spatial and temporal sets of context-dependent interactions, generating a high-level network when linked together, which incorporates contextual information without the need for molecular interaction data. Analysis of the pathway network revealed linked communities representing functional relationships, comparable to those found in molecular networks, including metabolism, signaling, immunity, and the cell cycle. We mapped a range of diseases onto this network and find that pathways associated with diseases tend to be functionally connected, highlighting the perturbed functions that result in disease phenotypes. We demonstrated that disease pathways cluster within the network. We then examined the distribution of cancer pathways and showed that cancer pathways tend to localize within the signaling, DNA processes and immune modules, although some cancer-associated nodes are found in other network regions. Altogether, we generated a high-confidence functional network, which avoids some of the shortcomings faced by conventional molecular models. Our representation provides an intuitive functional interpretation of cellular organization, which relies only on high-quality pathway and Gene Ontology data. The network is available at https://data.mendeley.com/datasets/3pbwkxjxg9/1.

Item Type:Articles
Additional Information:This work has been supported by the Biotechnology and Biological Sciences Research Council DTP [BB/J014478/1] and the Royal Society International Exchange Grant (IE160248).
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Robertson, Professor David
Authors: Stoney, R., Robertson, D. L., Nenadic, G., and Schwartz, J.-M.
College/School:College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
Journal Name:npj Systems Biology and Applications
Publisher:Nature Research
ISSN:2056-7189
ISSN (Online):2056-7189
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in npj Systems Biology and Applications 4:22
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record