Predict genome-scale fluxes based solely on enzyme abundance by a novel Hyper-Cube Shrink Algorithm

TL;DR

This paper introduces the Hyper-Cube Shrink Algorithm (HCSA), a novel method that predicts genome-scale metabolic fluxes solely based on enzyme abundance, integrating enzymatic properties into FBA models.

Contribution

The study presents a new algorithm that incorporates enzyme properties into FBA, enabling large-scale metabolic control analysis and flux prediction from enzyme data.

Findings

HCSA accurately predicts knockout strain fluxes.

It effectively estimates flux changes with enzyme activity adjustments.

Successfully applied to genome-scale yeast networks.

Abstract

One of the long-expected goals of genome-scale metabolic modeling is to evaluate the influence of the perturbed enzymes to the flux distribution. Both ordinary differential equation (ODE) models and the constraint-based models, like Flux balance analysis (FBA), lack of the room of performing metabolic control analysis (MCA) for large-scale networks. In this study, we developed a Hyper-Cube Shrink Algorithm (HCSA) to incorporate the enzymatic properties to the FBA model by introducing a pseudo reaction constrained by enzymatic parameters. Our algorithm was able to handle not only prediction of knockout strains but also strains with quantitative adjustment of expression level or activity. We first demonstrate the concept by applying HCSA to a simplest three-node network. Then we validate its prediction by comparing with ODE and with a synthetic network in Saccharomyces cerevisiae producing voilacein and analogues. Finally we show its capability of predicting the flux distribution in genome-scale networks by applying it to the sporulation in yeast.