Improved flux profiling in genome-scale modeling of human cell metabolism

TL;DR

A new regression-based method improves genome-scale metabolic modeling of human cells by providing more accurate flux constraints.

Contribution

A regression-based method using multi-time point data outperforms existing methods for determining exchange fluxes in human cell metabolism models.

Findings

The CORE method is unreliable for determining exchange fluxes in human cell metabolism models.

REGP-calculated fluxes constrain genome-scale models to biologically plausible solutions.

REGP satisfies steady-state assumptions required for metabolic simulations.

Abstract

Understanding human cell metabolism through genome-scale flux profiling is of interest to diverse research areas of human health and disease. Metabolic modeling using genome-scale metabolic models (GEMs) has the potential to achieve this, but has been limited by a lack of appropriate input data as model constraints. Here, we compare the commonly used consumption and release (CORE) method to a regression-based method (regression during exponential growth phase; REGP). We found that the CORE method is not reliable despite being prevalent in human studies, whereas the exchange fluxes determined by REGP provide constraints that substantially improve GEM simulations for human cell lines. Our results show that the GEM-simulated feasible flux space is constrained to a biologically plausible region, allowing an exploration of the basic organizing principles of the feasible flux space. These improvements help to fulfill the promise of GEMs as a valuable tool in the study of human metabolism and future development of translational applications.

•Regression-based method satisfies the steady-state assumption required for FBA/FVA•REGP- and CORE-calculated exchange fluxes differ substantially•REGP-calculated fluxes constrain GEM models to biologically plausible solutions

Regression-based method satisfies the steady-state assumption required for FBA/FVA

REGP- and CORE-calculated exchange fluxes differ substantially

REGP-calculated fluxes constrain GEM models to biologically plausible solutions

As cellular metabolism plays a central role in human health and disease, genome-scale flux profiling is of interest to a wide range of research fields. Genome-scale metabolic modeling is a promising computational tool that can be used to simulate intracellular metabolic fluxes in human cells. However, these models are known to be sensitive to the quality of the constraints, which are often calculated based on single-time point data that violate steady-state assumptions. In this paper, we investigate the potential of a regression-based method using multi-time point data to enable more accurate metabolic simulations.

Huijer et al. present a regression-based method to accurately determine metabolic exchange fluxes in human cells. These fluxes provide reliable constraints for metabolic model simulations to study human metabolism and disease.

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