Integration of single-cell RNA-seq data into population models to characterize cancer metabolism.

ABSTRACT: Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profile

SUBMITTER: Damiani C 

PROVIDER: S-EPMC6413955 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature