Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints
Journal article, 2017

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.

fermentative capacity

escherichia-coli

saccharomyces-cerevisiae

quantification

expression

growth-rate

evolutionary

quantitative prediction

protein

integration

Author

Benjamin José Sanchez Barja

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

C. Zhang

East China University of Science and Technology

Royal Institute of Technology (KTH)

Avlant Nilsson

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Petri-Jaan Lahtvee

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Eduard Kerkhoven

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Molecular Systems Biology

1744-4292 (ISSN)

Vol. 13 8 Article no 935 - 935

Subject Categories

Bioinformatics and Systems Biology

DOI

10.15252/msb.20167411

More information

Latest update

2/26/2018