Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions.

ABSTRACT:

Background

Genome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.

Results

Based on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints

SUBMITTER: Zhou J 

PROVIDER: S-EPMC8247156 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature