publication . Article . Other literature type . 2017

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

Benjamín J Sánchez; Cheng Zhang; Avlant Nilsson; Petri‐Jaan Lahtvee; Eduard J Kerkhoven; Jens Nielsen;
Open Access English
  • Published: 01 Jan 2017
  • Publisher: KTH, Science for Life Laboratory, SciLifeLab
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...
Subjects
free text keywords: enzyme kinetics, flux balance analysis, molecular crowding, proteomics, Saccharomyces cerevisiae, Oceanography, Hydrology and Water Resources, Oceanografi, hydrologi och vattenresurser, Article, Articles, Genome-Scale & Integrative Biology, Metabolism, Methods & Resources, General Biochemistry, Genetics and Molecular Biology, Computational Theory and Mathematics, General Immunology and Microbiology, Applied Mathematics, General Agricultural and Biological Sciences, Information Systems, Quantitative proteomics, Enzyme, chemistry.chemical_classification, chemistry, Bioinformatics, Biology, Metabolic pathway, biology.organism_classification, Metabolic engineering, Yeast
Funded by
EC| DD-DeCaF
Project
DD-DeCaF
Bioinformatics Services for Data-Driven Design of Cell Factories and Communities
  • Funder: European Commission (EC)
  • Project Code: 686070
  • Funding stream: H2020 | RIA
,
EC| CHASSY
Project
CHASSY
Model-Based Construction And Optimisation Of Versatile Chassis Yeast Strains For Production Of Valuable Lipid And Aromatic Compounds
  • Funder: European Commission (EC)
  • Project Code: 720824
  • Funding stream: H2020 | RIA
66 references, page 1 of 5

Adadi R, Volkmer B, Milo R, Heinemann M, Shlomi T (2012) Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Comput Biol 8: e1002575 22792053 [OpenAIRE] [PubMed]

Aung HW, Henry SA, Walker LP (2013) Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism. Ind Biotechnol 9: 215–228

Bar‐Even A, Noor E, Savir Y, Liebermeister W, Davidi D, Tawfik DS, Milo R (2011) The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50: 4402–4410 21506553 [PubMed]

Basan M, Hui S, Okano H, Zhang Z, Shen Y, Williamson JR, Hwa T (2015) Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528: 99–104 26632588 [OpenAIRE] [PubMed]

Beg QK, Vazquez A, Ernst J, de Menezes MA, Bar‐Joseph Z, Barabási AL, Oltvai ZN (2007) Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci USA 104: 12663–12668 17652176 [OpenAIRE] [PubMed]

Berkhout J, Bosdriesz E, Nikerel E, Molenaar D, de Ridder D, Teusink B, Bruggeman FJ (2013) How biochemical constraints of cellular growth shape evolutionary adaptations in metabolism. Genetics 194: 505–512 23535382 [OpenAIRE] [PubMed]

Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S, Schneider M (2003) The SWISS‐PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 31: 365–370 12520024 [OpenAIRE] [PubMed]

Bordel S, Agren R, Nielsen J (2010) Sampling the solution space in genome‐scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput Biol 6: 16

Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila‐Campilo I, Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K, Pavlovic V, Pico AR et al (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2: 2366–2382 17947979 [OpenAIRE] [PubMed]

D'Amore T, Russell I, Stewart GG (1989) Sugar utilization by yeast during fermentation. J Ind Microbiol 4: 315–323

Davidi D, Noor E, Liebermeister W, Bar‐Even A, Flamholz A, Tummler K, Barenholz U, Goldenfeld M, Shlomi T, Milo R (2016) Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k cat measurements. Proc Natl Acad Sci USA 113: 3401–3406 26951675 [OpenAIRE] [PubMed]

Famili I, Forster J, Nielsen J, Palsson BØ (2003) Saccharomyces cerevisiae phenotypes can be predicted by using constraint‐based analysis of a genome‐scale reconstructed metabolic network. Proc Natl Acad Sci USA 100: 13134–13139 14578455 [OpenAIRE] [PubMed]

Farzadfard F, Perli SD, Lu TK (2013) Tunable and multifunctional eukaryotic transcription factors based on CRISPR/Cas. ACS Synth Biol 2: 604–613 23977949 [OpenAIRE] [PubMed]

Feizi A, Österlund T, Petranovic D, Bordel S, Nielsen J (2013) Genome‐scale modeling of the protein secretory machinery in yeast. PLoS One 8: e63284 2366760 1 [OpenAIRE] [PubMed]

Goelzer A, Muntel J, Chubukov V, Jules M, Prestel E, Nölker R, Mariadassou M, Aymerich S, Hecker M, Noirot P, Becher D, Fromion V (2015) Quantitative prediction of genome‐wide resource allocation in bacteria. Metab Eng 32: 232–243 26498510 [PubMed]

66 references, page 1 of 5
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...
Subjects
free text keywords: enzyme kinetics, flux balance analysis, molecular crowding, proteomics, Saccharomyces cerevisiae, Oceanography, Hydrology and Water Resources, Oceanografi, hydrologi och vattenresurser, Article, Articles, Genome-Scale & Integrative Biology, Metabolism, Methods & Resources, General Biochemistry, Genetics and Molecular Biology, Computational Theory and Mathematics, General Immunology and Microbiology, Applied Mathematics, General Agricultural and Biological Sciences, Information Systems, Quantitative proteomics, Enzyme, chemistry.chemical_classification, chemistry, Bioinformatics, Biology, Metabolic pathway, biology.organism_classification, Metabolic engineering, Yeast
Funded by
EC| DD-DeCaF
Project
DD-DeCaF
Bioinformatics Services for Data-Driven Design of Cell Factories and Communities
  • Funder: European Commission (EC)
  • Project Code: 686070
  • Funding stream: H2020 | RIA
,
EC| CHASSY
Project
CHASSY
Model-Based Construction And Optimisation Of Versatile Chassis Yeast Strains For Production Of Valuable Lipid And Aromatic Compounds
  • Funder: European Commission (EC)
  • Project Code: 720824
  • Funding stream: H2020 | RIA
66 references, page 1 of 5

Adadi R, Volkmer B, Milo R, Heinemann M, Shlomi T (2012) Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Comput Biol 8: e1002575 22792053 [OpenAIRE] [PubMed]

Aung HW, Henry SA, Walker LP (2013) Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism. Ind Biotechnol 9: 215–228

Bar‐Even A, Noor E, Savir Y, Liebermeister W, Davidi D, Tawfik DS, Milo R (2011) The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50: 4402–4410 21506553 [PubMed]

Basan M, Hui S, Okano H, Zhang Z, Shen Y, Williamson JR, Hwa T (2015) Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528: 99–104 26632588 [OpenAIRE] [PubMed]

Beg QK, Vazquez A, Ernst J, de Menezes MA, Bar‐Joseph Z, Barabási AL, Oltvai ZN (2007) Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc Natl Acad Sci USA 104: 12663–12668 17652176 [OpenAIRE] [PubMed]

Berkhout J, Bosdriesz E, Nikerel E, Molenaar D, de Ridder D, Teusink B, Bruggeman FJ (2013) How biochemical constraints of cellular growth shape evolutionary adaptations in metabolism. Genetics 194: 505–512 23535382 [OpenAIRE] [PubMed]

Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O'Donovan C, Phan I, Pilbout S, Schneider M (2003) The SWISS‐PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res 31: 365–370 12520024 [OpenAIRE] [PubMed]

Bordel S, Agren R, Nielsen J (2010) Sampling the solution space in genome‐scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput Biol 6: 16

Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila‐Campilo I, Creech M, Gross B, Hanspers K, Isserlin R, Kelley R, Killcoyne S, Lotia S, Maere S, Morris J, Ono K, Pavlovic V, Pico AR et al (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2: 2366–2382 17947979 [OpenAIRE] [PubMed]

D'Amore T, Russell I, Stewart GG (1989) Sugar utilization by yeast during fermentation. J Ind Microbiol 4: 315–323

Davidi D, Noor E, Liebermeister W, Bar‐Even A, Flamholz A, Tummler K, Barenholz U, Goldenfeld M, Shlomi T, Milo R (2016) Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k cat measurements. Proc Natl Acad Sci USA 113: 3401–3406 26951675 [OpenAIRE] [PubMed]

Famili I, Forster J, Nielsen J, Palsson BØ (2003) Saccharomyces cerevisiae phenotypes can be predicted by using constraint‐based analysis of a genome‐scale reconstructed metabolic network. Proc Natl Acad Sci USA 100: 13134–13139 14578455 [OpenAIRE] [PubMed]

Farzadfard F, Perli SD, Lu TK (2013) Tunable and multifunctional eukaryotic transcription factors based on CRISPR/Cas. ACS Synth Biol 2: 604–613 23977949 [OpenAIRE] [PubMed]

Feizi A, Österlund T, Petranovic D, Bordel S, Nielsen J (2013) Genome‐scale modeling of the protein secretory machinery in yeast. PLoS One 8: e63284 2366760 1 [OpenAIRE] [PubMed]

Goelzer A, Muntel J, Chubukov V, Jules M, Prestel E, Nölker R, Mariadassou M, Aymerich S, Hecker M, Noirot P, Becher D, Fromion V (2015) Quantitative prediction of genome‐wide resource allocation in bacteria. Metab Eng 32: 232–243 26498510 [PubMed]

66 references, page 1 of 5
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publication . Article . Other literature type . 2017

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

Benjamín J Sánchez; Cheng Zhang; Avlant Nilsson; Petri‐Jaan Lahtvee; Eduard J Kerkhoven; Jens Nielsen;