Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures

Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.

Automated summary

Genome-scale metabolic models (GEMs) have various applications in different fields, from biotechnology to systems medicine. This article provides an overview of popular algorithms for automated reconstruction of context-specific GEMs using high-throughput experimental data. It also discusses different datasets used in the process and protocols for further automating model reconstruction and validation. Furthermore, recent COVID-19 applications of context-specific GEMs are described, focusing on the analysis of metabolic implications, identification of biomarkers, and potential drug targets.