skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

Abstract

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 ofmore » model-based design in metabolic engineering.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [3]
  1. Chalmers Univ. of Technology, Gothenburg (Sweden)
  2. KTH Royal Inst. of Technology, Stockholm (Sweden); East China Univ. of Science and Technology, Shanghai (China)
  3. Chalmers Univ. of Technology, Gothenburg (Sweden); Technical Univ. of Denmark, Lyngby (Denmark)
Publication Date:
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1423877
Grant/Contract Number:
sc0008744
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Molecular Systems Biology
Additional Journal Information:
Journal Volume: 13; Journal Issue: 8; Journal ID: ISSN 1744-4292
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Enzyme Kinetics; Flux Balance Analysis; Molecular Crowding; Proteomics; Saccharomyces Cerevisiae

Citation Formats

Sánchez, Benjamín J., Zhang, Cheng, Nilsson, Avlant, Lahtvee, Petri‐Jaan, Kerkhoven, Eduard J., and Nielsen, Jens. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. United States: N. p., 2017. Web. doi:10.15252/msb.20167411.
Sánchez, Benjamín J., Zhang, Cheng, Nilsson, Avlant, Lahtvee, Petri‐Jaan, Kerkhoven, Eduard J., & Nielsen, Jens. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. United States. doi:10.15252/msb.20167411.
Sánchez, Benjamín J., Zhang, Cheng, Nilsson, Avlant, Lahtvee, Petri‐Jaan, Kerkhoven, Eduard J., and Nielsen, Jens. Wed . "Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints". United States. doi:10.15252/msb.20167411. https://www.osti.gov/servlets/purl/1423877.
@article{osti_1423877,
title = {Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints},
author = {Sánchez, Benjamín J. and Zhang, Cheng and Nilsson, Avlant and Lahtvee, Petri‐Jaan and Kerkhoven, Eduard J. and Nielsen, Jens},
abstractNote = {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.},
doi = {10.15252/msb.20167411},
journal = {Molecular Systems Biology},
number = 8,
volume = 13,
place = {United States},
year = {Wed Mar 08 00:00:00 EST 2017},
month = {Wed Mar 08 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 3works
Citation information provided by
Web of Science

Save / Share:
  • Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated withmore » microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparisonto prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model under conditions in which one or more nutrients were limiting. The fitted Monod kinetic model was also applied at the Darcy scale; that is, to simulate average reaction processes at the scale of the entire pore-scale model domain. As we expected, even under excess nutrient conditions for which the Monod and genome-scale models predicted equal reaction rates at the pore scale, the Monod model over-predicted the rates of biomass growth and iron and acetate utilization when applied at the Darcy scale. This discrepancy is caused by an inherent assumption of perfect mixing over the Darcy-scale domain, which is clearly violated in the pore-scale models. These results help to explain the need to modify the flux constraint parameters in order to match observations in previous applications of the genome-scale model at larger scales. These results also motivate further investigation of quantitative multi-scale relationships between fundamental behavior at the pore scale (where genome-scale models are appropriately applied) and observed behavior at larger scales (where predictions of reactive transport phenomena are needed).« less
  • Reversibility constraints are one aspect of genome-scale metabolic models that has received significant attention recently. This study explores the impact of complete removal of reversibility constraints on the gene essentiality and growth phenotype predictions generated using three published genome-scale metabolic models: the iJR904, the iAF1260, and the iBsu1103. In all three models, the accuracy in predicting essential genes declined significantly with the relaxation of reversibility constraints, while the accuracy in predicting nonessential genes increased only for the iJR904 and iAF1260 model. Additionally, the number of inactive reactions in all models declined substantially with the relaxation of the reversibility constraints. Thismore » study rapidly reveals the extent to which the reversibility constraints included in a metabolic model have been optimized, and it indicates those incorrect model predictions that may be repaired and those correct model predictions that may be broken by increasing the number of reversible reactions in a model.« less
  • Quantitative numerical simulation codes known as reactive transport models are widely used for simulating the hydrologic transport and geochemical speciation of dissolved constituents in the subsurface (Steefel et al., 2005). Because the activity of microorganisms strongly influences the fate of many constituents, both organic and inorganic, such models often include microbially-mediated reactions in their reaction networks (Hunter et al., 1998; Burgos et al., 2002; Fang et al., 2006; Scheibe et al., 2006; Yabusaki et al., 2007). However, the canonical form and stoichiometry of microbial reactions, reaction rate formulations and parameters, and biomass growth yield coefficients are prescribed a priori andmore » applied over the entire range of simulated conditions. This approach does not account for the fact that fundamental microbial functions vary in response to local variations in environmental conditions(Stewart and Franklin, 2008). Multiple alternative reaction pathways are encoded in microbial genomes; specific pathways become active or inactive in response to, for example, nutrient limitation. Recent advances in genomic analysis allow us to define cellular metabolic networks, and accurate predictions of active pathways and reaction fluxes have been made using constraint-based metabolic models (Mahadevan et al., 2002; Price et al., 2003; Reed and Palsson, 2003; Mahadevan et al., 2006). Here, we demonstrate for the first time a methodology of coupling constraint-based metabolic models with reactive transport models. Our approach integrates advanced microbiological characterization, hydrology, and geochemistry in a powerful manner that will significantly improve subsurface reactive transport models.« less
  • Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curatedmore » reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. Furthermore, the model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.« less