A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models

Andrea Cabbia; Peter A J Hilbers; Natal A W van Riel

doi:10.1016/j.patter.2020.100080

A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models

Patterns (N Y). 2020 Aug 6;1(6):100080. doi: 10.1016/j.patter.2020.100080. eCollection 2020 Sep 11.

Authors

Andrea Cabbia¹, Peter A J Hilbers¹, Natal A W van Riel^{1

2}

Affiliations

¹ Computational Biology, Eindhoven University of Technology, Groene Loper 5, 5612 AE Eindhoven, the Netherlands.
² Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, the Netherlands.

Abstract

Gene expression and protein abundance data of cells or tissues belonging to healthy and diseased individuals can be integrated and mapped onto genome-scale metabolic networks to produce patient-derived models. As the number of available and newly developed genome-scale metabolic models increases, new methods are needed to objectively analyze large sets of models and to identify the determinants of metabolic heterogeneity. We developed a distance-based workflow that combines consensus machine learning and metabolic modeling techniques and used it to apply pattern recognition algorithms to collections of genome-scale metabolic models, both microbial and human. Model composition, network topology and flux distribution provide complementary aspects of metabolic heterogeneity in patient-specific genome-scale models of skeletal muscle. Using consensus clustering analysis we identified the metabolic processes involved in the individual responses to endurance training in older adults.

Keywords: distance; genome-scale metabolic models; heterogeneity; machine learning; metabolism.