| Sonstiges: |
- Nachgewiesen in: MEDLINE
- Sprachen: English
- Publication Type: Journal Article; Review
- Language: English
- [Nat Protoc] 2024 Mar; Vol. 19 (3), pp. 629-667. <i>Date of Electronic Publication: </i>2024 Jan 18.
- MeSH Terms: Metabolic Engineering* / methods ; Models, Biological* ; Computer Simulation ; Saccharomyces cerevisiae / genetics ; Metabolic Networks and Pathways
- References: Kim, W. J., Kim, H. U. & Lee, S. Y. Current state and applications of microbial genome-scale metabolic models. Curr. Opin. Syst. Biol. 2, 10–18 (2017). ; Chen, Y., Li, G. & Nielsen, J. Genome-scale metabolic modeling from yeast to human cell models of complex diseases: latest advances and challenges. Methods Mol. Biol. 2049, 329–345 (2019). (PMID: 31602620) ; Clark, T. J., Guo, L., Morgan, J. & Schwender, J. Modeling plant metabolism: from network reconstruction to mechanistic models. Annu. Rev. Plant Biol. 71, 303–326 (2020). (PMID: 32017600) ; Fang, X., Lloyd, C. J. & Palsson, B. O. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-00440-4 (2020). ; Heinken, A., Basile, A., Hertel, J., Thinnes, C. & Thiele, I. Genome-scale metabolic modeling of the human microbiome in the era of personalized medicine. Annu Rev. Microbiol 75, 199–222 (2021). (PMID: 34314593) ; Lewis, N. E., Nagarajan, H. & Palsson, B. O. Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods. Nat. Rev. Microbiol. 10, 291–305 (2012). (PMID: 223671183536058) ; Lloyd, C. J. et al. COBRAme: a computational framework for genome-scale models of metabolism and gene expression. PLoS Comput. Biol. 14, e1006302 (2018). (PMID: 299756816049947) ; Goelzer, A. et al. Quantitative prediction of genome-wide resource allocation in bacteria. Metab. Eng. 32, 232–243 (2015). (PMID: 26498510) ; Salvy, P. & Hatzimanikatis, V. The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models. Nat. Commun. 11, 30 (2020). (PMID: 319377636959363) ; Chen, Y. et al. Proteome constraints reveal targets for improving microbial fitness in nutrient‐rich environments. Mol. Syst. Biol. 17, e10093 (2021). (PMID: 338215498022198) ; Beg, Q. K. et al. 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 (2007). (PMID: 176521761937523) ; Adadi, R., Volkmer, B., Milo, R., Heinemann, M. & Shlomi, T. Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Comput. Biol. 8, e1002575 (2012). (PMID: 227920533390398) ; Sánchez, B. J. et al. Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints. Mol. Syst. Biol. 13, 935 (2017). (PMID: 287790055572397) ; Bekiaris, P. S. & Klamt, S. Automatic construction of metabolic models with enzyme constraints. BMC Bioinforma. 21, 19 (2020). ; Mao, Z. et al. ECMpy, a simplified workflow for constructing enzymatic constrained metabolic network model. Biomolecules 12, 65 (2022). (PMID: 350532138773657) ; Domenzain, I. et al. Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0. Nat. Commun. 13, 1–13 (2022). ; Chen, Y. & Nielsen, J. Mathematical modelling of proteome constraints within metabolism. Curr. Opin. Syst. Biol. 25, 50–56 (2021). ; Kerkhoven, E. J. Advances in constraint-based models: methods for improved predictive power based on resource allocation constraints. Curr. Opin. Microbiol. 68, 102168 (2022). (PMID: 35691074) ; Chen, Y. & Nielsen, J. Energy metabolism controls phenotypes by protein efficiency and allocation. Proc. Natl Acad. Sci. USA 116, 17592–17597 (2019). (PMID: 314059846717264) ; Gustafsson, J. et al. Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth. Preprint at bioRxiv https://doi.org/10.1101/2022.02.08.479584 (2022). ; Chen, Y. & Nielsen, J. Yeast has evolved to minimize protein resource cost for synthesizing amino acids. Proc. Natl Acad. Sci. USA 119, e2114622119 (2022). (PMID: 350427998795554) ; Chang, A. et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49, D498–D508 (2021). (PMID: 33211880) ; Davidi, D. & Milo, R. Lessons on enzyme kinetics from quantitative proteomics. Curr. Opin. Biotechnol. 46, 81–89 (2017). (PMID: 28288339) ; Li, F. et al. Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nat. Catal. https://doi.org/10.1038/s41929-022-00798-z (2022). ; Sulheim, S. et al. Enzyme-constrained models and omics analysis of Streptomyces coelicolor reveal metabolic changes that enhance heterologous production. iScience 23, 101525 (2020). (PMID: 329421747501462) ; Zhou, J., Zhuang, Y. & Xia, J. Integration of enzyme constraints in a genome-scale metabolic model of Aspergillus niger improves phenotype predictions. Microb. Cell Fact. 20, 125 (2021). (PMID: 341931178247156) ; Ye, C. et al. Improving lysine production through construction of an Escherichia coli enzyme‐constrained model. Biotechnol. Bioeng. 117, 3533–3544 (2020). (PMID: 32648933) ; Chen, Y. et al. Genome‐scale modeling for Bacillus coagulans to understand the metabolic characteristics. Biotechnol. Bioeng. 117, 3545–3558 (2020). (PMID: 32648961) ; Lu, H. et al. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Mol. Syst. Biol. 17, e10427 (2021). (PMID: 346769848532513) ; Ishchuk, O. P. et al. Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 119, e2108245119 (2022). (PMID: 358584109335255) ; Li, Z. et al. Systems engineering of Escherichia coli for high-level shikimate production. Metab. Eng. 75, 1–11 (2023). (PMID: 36328295) ; Bujdoš, D. et al. Engineering of Pseudomonas putida for accelerated co-utilization of glucose and cellobiose yields aerobic overproduction of pyruvate explained by an upgraded metabolic model. Metab. Eng. 75, 29–46 (2023). (PMID: 36343876) ; Domenzain, I., Lu, Y., Shi, J., Lu, H. & Nielsen, J. Computational biology predicts metabolic engineering targets for increased production of 102 valuable chemicals in yeast. Preprint at bioRxiv https://doi.org/10.1101/2023.01.31.526512 (2023). ; Heirendt, L. et al. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. https://doi.org/10.1038/s41596-018-0098-2 (2019). ; Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis? Nat. Biotechnol. 28, 245 (2010). (PMID: 202124903108565) ; Gudmundsson, S. & Thiele, I. Computationally efficient flux variability analysis. BMC Bioinforma. 11, 489 (2010). ; Choi, H. S., Lee, S. Y., Kim, T. Y. & Woo, H. M. In silico identification of gene amplification targets for improvement of lycopene production. Appl. Environ. Microbiol. 76, 3097–3105 (2010). (PMID: 203483052869140) ; Agren, R. et al. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol. Syst. Biol. 10, 721 (2014). (PMID: 246466614017677) ; Cho, J. S., Gu, C., Han, T. H., Ryu, J. Y. & Lee, S. Y. Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring. Curr. Opin. Syst. Biol. 15, 1–11 (2019). ; Robinson, J. L. et al. An atlas of human metabolism. Sci. Signal. 13, 1482 (2020). ; Yeo, H. C., Hong, J., Lakshmanan, M. & Lee, D. Y. Enzyme capacity-based genome scale modelling of CHO cells. Metab. Eng. 60, 138–147 (2020). (PMID: 32330653) ; Wendering, P. & Nikoloski, Z. Genome-scale modeling specifies the metabolic capabilities of Rhizophagus irregularis. mSystems 7, e0121621 (2022). (PMID: 35076269) ; Wu, K. et al. ecBSU1: A genome-scale enzyme-constrained model of Bacillus subtilis based on the ECMpy workflow. Microorganisms 11, 178 (2023). (PMID: 366774699864840) ; Österberg, L. et al. A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism. PLoS Comput. Biol. 17, e1008891 (2021). (PMID: 338360008059808) ; Chen, Y., Li, F. & Nielsen, J. Genome-scale modeling of yeast metabolism: retrospectives and perspectives. FEMS Yeast Res 22, 1–9 (2022). ; The UniProt Consortium. UniProt: the universal protein knowledgebase in 2023. Nucleic Acids Res. 51, D523–D531 (2023). ; Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M. & Tanabe, M. KEGG: integrating viruses and cellular organisms. Nucleic Acids Res 49, D545–D551 (2021). (PMID: 33125081) ; Thiele, I. & Palsson, B. Ø. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93–121 (2010). (PMID: 200573833125167) ; Norsigian, C. J., Fang, X., Seif, Y., Monk, J. M. & Palsson, B. O. A workflow for generating multi-strain genome-scale metabolic models of prokaryotes. Nat. Protoc. https://doi.org/10.1038/s41596-019-0254-3 (2019). (PMID: 10.1038/s41596-019-0254-3318630767017905) ; Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 46, 7542–7553 (2018). (PMID: 301929796125623) ; Wang, H. et al. RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput. Biol. 14, e1006541 (2018). (PMID: 303357856207324) ; Davidi, D. et al. Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements. Proc. Natl Acad. Sci. USA 113, 3401–3406 (2016). (PMID: 269516754812741) ; Küken, A., Gennermann, K. & Nikoloski, Z. Characterization of maximal enzyme catalytic rates in central metabolism of Arabidopsis thaliana. Plant J. 103, 2168–2177 (2020). (PMID: 32656814) ; Heckmann, D. et al. Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers. Proc. Natl Acad. Sci. USA 117, 23182–23190 (2020). (PMID: 328736457502767) ; Chen, Y. & Nielsen, J. In vitro turnover numbers do not reflect in vivo activities of yeast enzymes. Proc. Natl Acad. Sci. USA 118, 2021 (2021). ; Xu, R., Razaghi-Moghadam, Z. & Nikoloski, Z. Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli. Bioinformatics https://doi.org/10.1093/BIOINFORMATICS/BTAB575 (2021). (PMID: 10.1093/BIOINFORMATICS/BTAB5753487603210060710) ; Heckmann, D. et al. Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat. Commun. 9, 5252 (2018). (PMID: 305319876286351) ; Schendel, F., Mueller, E., Stubbe, J., Shiau, A. & Smith, J. Formylglycinamide ribonucleotide synthetase from Escherichia coli: cloning, sequencing, overproduction, isolation, and characterization. Biochemistry 28, 2459–2471 (1989). (PMID: 2659070) ; Sánchez, B. J. et al. Benchmarking accuracy and precision of intensity-based absolute quantification of protein abundances in Saccharomyces cerevisiae. Proteomics 21, e2000093 (2021). (PMID: 33452728) ; Van Hoek, P., Van Dijken, J. P. & Pronk, J. T. Effect of specific growth rate on fermentative capacity of baker’s yeast. Appl. Environ. Microbiol. 64, 4226–4233 (1998). (PMID: 9797269106631) ; Heublein, M. et al. The novel component Kgd4 recruits the E3 subunit to the mitochondrial α-ketoglutarate dehydrogenase. Mol. Biol. Cell 25, 3342–3349 (2014). (PMID: 251651434214781) ; Sánchez, B. J., Li, F., Kerkhoven, E. J. & Nielsen, J. SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework. BMC Syst. Biol. 13, 4 (2019). (PMID: 306349576330394) ; Lewis, N. E. et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol. Syst. Biol. 6, 390 (2010). (PMID: 206646362925526)
- Grant Information: NNF20CC0035580 Novo Nordisk Fonden (Novo Nordisk Foundation); 814650 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020); 720824 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020); 2019-04624 Vetenskapsrådet (Swedish Research Council); 2018-00597 Svenska Forskningsrådet Formas (Swedish Research Council Formas)
- Entry Date(s): Date Created: 20240118 Date Completed: 20240311 Latest Revision: 20240311
- Update Code: 20240311
|
