Genome-scale metabolic models applied for human health and biopharmaceutical engineering

Abstract

Over the last 15 years, genome-scale metabolic models (GEMs) have been reconstructed for human and model animals, such as mouse and rat, to systematically understand metabolism, simulate multicellular or multi-tissue interplay, understand human diseases, and guide cell factory design for biopharmaceutical protein production. Here, we describe how metabolic networks can be represented using stoichiometric matrices and well-defined constraints for flux simulation. Then, we review the history of GEM development for quantitative understanding of Homo sapiens and other relevant animals, together with their applications. We describe how model develops from H. sapiens to other animals and from generic purpose to precise context-specific simulation. The progress of GEMs for animals greatly expand our systematic understanding of metabolism in human and related animals. We discuss the difficulties and present perspectives on the GEM development and the quest to integrate more biological processes and omics data for future research and translation. We truly hope that this review can inspire new models developed for other mammalian organisms and generate new algorithms for integrating big data to conduct more in-depth analysis to further make progress on human health and biopharmaceutical engineering.

Keywords

constraint-based modeling / disease / genome-scale metabolic model / metabolism

References

[1] Schilling CH, Palsson BO. Assessment of the metabolic capabilities of Haemophilus influenzae Rd through a genomescale pathway analysis. J Theor Biol. 2000;203:249–83.

[2] Gu C, Kim GB, Kim WJ, Kim HU, Lee SY. Current status and applications of genome-scale metabolic models. Genome Biol. 2019;20(1):121.

[3] Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotechnol. 2010;28(3):245–8.

[4] Choi HS, Lee SY, Kim TY, Woo HM. In silico identification of gene amplification targets for improvement of lycopene production. Appl Environ Microbiol. 2010;76(10):3097–105.

[5] Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol. 2010;6(1):390.

[6] Lopes H, Rocha I. Genome-scale modeling of yeast: chronology, applications and critical perspectives. FEMS Yeast Res. 2017;17(5):fox050.

[7] Vucic EA, Thu KL, Robison K, Rybaczyk LA, Chari R, Alvarez CE, et al. Translating cancer ‘omics’ to improved outcomes. Genome Res. 2012;22(2):188–95.

[8] Melé M, Ferreira PG, Reverter F, DeLuca DS, Monlong J, Sammeth M, et al. The human transcriptome across tissues and individuals. Science. 2015;348(6235):660–5.

[9] Romero P, Wagg J, Green ML, Kaiser D, Krummenacker M, Karp PD. Computational prediction of human metabolic pathways from the complete human genome. Genome Biol. 2005;6(1):R2.

[10] Evsikov Av, Dolan ME, Genrich MP, Patek E, Bult CJ. MouseCyc: a curated biochemical pathways database for the laboratory mouse. Genome Biol. 2009;10(8):R84.

[11] Li S, Pozhitkov A, Ryan RA, Manning CS, Brown-Peterson N, Brouwer M. Constructing a fish metabolic network model. Genome Biol. 2010;11:R115.

[12] King ZA, Lloyd CJ, Feist AM, Palsson BO. Next-generation genome-scale models for metabolic engineering. Curr Opin Biotechnol. 2015;35:23–9.

[13] Thiele I, Palsson BØ. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5(1):93–121.

[14] 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. 2019;2049:329–45.

[15] Du H, Li M, Liu Y. Towards applications of genome-scale metabolic model-based approaches in designing synthetic microbial communities. Quant Biol. 2023;11(1):15–30.

[16] Gustafsson J, Robinson JL, Roshanzamir F, Jörnsten R, Kerkhoven EJ, Nielsen J. Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data. Proc Natl Acad Sci USA. 2023;120(6):e2217868120.

[17] Duarte NC, Becker SA, Jamshidi N, Thiele I, Mo ML, Vo TD, et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci U S A. 2007;104(6):1777–82.

[18] Sheikh K, Förster J, Nielsen LK. Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus. Biotechnol Prog. 2005;21(1):112–21.

[19] Quek L-E, Nielsen LK. On the reconstruction of the Mus musculus genome-scale metabolic network model. Genome Inform. 2008;21:89–100.

[20] Selvarasu S, Wong VVT, Karimi IA, Lee D-Y. Elucidation of metabolism in hybridoma cells grown in fed-batch culture by genome-scale modeling. Biotechnol Bioeng. 2009;102(5): 1494–504.

[21] Selvarasu S, Karimi IA, Ghim G-H, Lee D-Y. Genome-scale modeling and in silico analysis of mouse cell metabolic network. Mol Biosyst. 2010;6(1):152–61.

[22] Selvarasu S, Ho YS, Chong WPK, Wong NSC, Yusufi FNK, Lee YY, et al. Combined in silico modeling and metabolomics analysis to characterize fed-batch CHO cell culture. Biotechnol Bioeng. 2012;109(6):1415–29.

[23] Zhang C, Hua Q. Applications of genome-scale metabolic models in biotechnology and systems medicine. Front Physiol. 2015;6:413.

[24] Ma H, Sorokin A, Mazein A, Selkov A, Selkov E, Demin O, et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Mol Syst Biol. 2007;3(1):135.

[25] Hao T, Ma H-W, Zhao X-M, Goryanin I. Compartmentalization of the Edinburgh human metabolic network. BMC Bioinf. 2010;11(1):393.

[26] Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S, Aurich MK, et al. A community-driven global reconstruction of human metabolism. Nat Biotechnol. 2013;31(5):419–25.

[27] Swainston N, Smallbone K, Hefzi H, Dobson PD, Brewer J, Hanscho M, et al. Recon 2.2: from reconstruction to model of human metabolism. Metabolomics. 2016;12:1–7.

[28] Ryu JY, Kim HU, Lee SY. Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism. Proc Natl Acad Sci USA. 2017;114(45).

[29] Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Dräger A, Mih N, et al. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol. 2018;36(3): 272–81.

[30] Agren R, Bordel S, Mardinoglu A, Pornputtapong N, Nookaew I, Nielsen J. Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput Biol. 2012;8(5):e1002518.

[31] Mardinoglu A, Agren R, Kampf C, Asplund A, Uhlen M, Nielsen J. Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun. 2014;5(1):3083.

[32] Blais EM, Rawls KD, Dougherty Bv, Li ZI, Kolling GL, Ye P, et al. Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions. Nat Commun. 2017;8(1):14250.

[33] Robinson JL, Kocabaş P, Wang H, Cholley P-E, Cook D, Nilsson A, et al. An atlas of human metabolism. Sci Signal. 2020;13(624).

[34] Norsigian CJ, Fang X, Seif Y, Monk JM, Palsson BO. A workflow for generating multi-strain genome-scale metabolic models of prokaryotes. Nat Protoc. 2020;15:1–14.

[35] Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 2018;46(15):7542–53.

[36] Sigurdsson MI, Jamshidi N, Steingrimsson E, Thiele I, Palsson BØ. A detailed genome-wide reconstruction of mouse meta-bolism based on human Recon 1. BMC Syst Biol. 2010;4:1–13.

[37] Mardinoglu A, Shoaie S, Bergentall M, Ghaffari P, Zhang C, Larsson E, et al. The gut microbiota modulates host amino acid and glutathione metabolism in mice. Mol Syst Biol. 2015;11(10):834.

[38] Khodaee S, Asgari Y, Totonchi M, Karimi-Jafari MH. iMM1865: a new reconstruction of mouse genome-scale metabolic model. Sci Rep. 2020;10(1):6177.

[39] Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley P-E, et al. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci USA. 2021;118(30):e2102344118.

[40] Lieven C, Beber ME, Olivier BG, Bergmann FT, Ataman M, Babaei P, et al. MEMOTE for standardized genome-scale metabolic model testing. Nat Biotechnol. 2020;38(3):272–6.

[41] Hefzi H, Ang KS, Hanscho M, Bordbar A, Ruckerbauer D, Lakshmanan M, et al. A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism. Cell Syst. 2016;3(5):434.e8–443.e8.

[42] Yeo HC, Hong J, Lakshmanan M, Lee D-Y. Enzyme capacity-based genome scale modelling of CHO cells. Metab Eng. 2020;60:138–47.

[43] Bendiksen EÅ, Johnsen CA, Olsen HJ, Jobling M. Sustainable aquafeeds: progress towards reduced reliance upon marine ingredients in diets for farmed Atlantic salmon (Salmo salar L.). Aquaculture. 2011;314(1-4):132–9.

[44] Bekaert M. Reconstruction of Danio rerio metabolic model accounting for subcellular compartmentalisation. PLoS One. 2012;7(11):e49903.

[45] van Steijn L, Verbeek FJ, Spaink HP, Merks RMH. Predicting metabolism from gene expression in an improved wholegenome metabolic network model of Danio rerio. Zebrafish. 2019;16(4):348–62.

[46] Gao C, Yang J, Hao T, Li J, Sun J. Reconstruction of Litopenaeus vannamei genome-scale metabolic network model and nutritional requirements analysis of different shrimp commercial varieties. Front Genet. 2021;12:658109.

[47] Yilmaz LS, Walhout AJM. A Caenorhabditis elegans genome-scale metabolic network model. Cell Syst. 2016;2(5):297–311.

[48] Yilmaz LS, Li X, Nanda S, Fox B, Schroeder F, Walhout AJ. Modeling tissue-relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels. Mol Syst Biol. 2020;16(10):e9649.

[49] Salehabadi E, Motamedian E, Shojaosadati SA. Reconstruction of a generic genome-scale metabolic network for chicken: investigating network connectivity and finding potential biomarkers. PLoS One. 2022;17(3):e0254270.

[50] Becker SA, Palsson BO. Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol. 2008;4(5):e1000082.

[51] Zur H, Ruppin E, Shlomi T. iMAT: an integrative metabolic analysis tool. Bioinformatics. 2010;26(24):3140–2.

[52] Jerby L, Shlomi T, Ruppin E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Mol Syst Biol. 2010;6(1):401.

[53] Agren R, Mardinoglu A, Asplund A, Kampf C, Uhlen M, Nielsen J. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol. 2014;10(3):721.

[54] Wang Y, Eddy JA, Price ND. Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol. 2012;6:1–16.

[55] Gopalakrishnan S, Joshi CJ, Valderrama-Gómez MÁ, Icten E, Rolandi P, Johnson W, et al. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng. 2023;75:181–91.

[56] Režen T, Martins A, Mraz M, Zimic N, Rozman D, Moškon M. Integration of omics data to generate and analyse COVID-19 specific genome-scale metabolic models. Comput Biol Med. 2022;145:105428.

[57] Opdam S, Richelle A, Kellman B, Li S, Zielinski DC, Lewis NE. A systematic evaluation of methods for tailoring genome-scale metabolic models. Cell Syst. 2017;4(3):318.e6–329.e6.

[58] Cook DJ, Nielsen J. Genome-scale metabolic models applied to human health and disease. WIREs Syst Biol Med. 2017;9(6):e1393.

[59] Cheng K, Martin-Sancho L, Pal LR, Pu Y, Riva L, Yin X, et al. Genome-scale metabolic modeling reveals SARS-CoV-2-induced metabolic changes and antiviral targets. Mol Syst Biol. 2021;17(11):e10260.

[60] Nanda P, Ghosh A. Genome scale-differential flux analysis reveals deregulation of lung cell metabolism on SARS-CoV-2 infection. PLoS Comput Biol. 2021;17(4):e1008860.

[61] Ambikan AT, Yang H, Krishnan S, Svensson Akusjärvi S, Gupta S, Lourda M, et al. Multi-omics personalized network analyses highlight progressive disruption of central metabolism associated with COVID-19 severity. Cell Syst. 2022;13(8):665.e4–681.e4.

[62] Manchel A, Mahadevan R, Bataller R, Hoek JB, Vadigepalli R. Genome-scale metabolic modeling reveals sequential dysregulation of glutathione metabolism in livers from patients with alcoholic hepatitis. Metabolites. 2022;12:1157.

[63] Väremo L, Scheele C, Broholm C, Mardinoglu A, Kampf C, Asplund A, et al. Proteome- and transcriptome-driven reconstruction of the human myocyte metabolic network and its use for identification of markers for diabetes. Cell Rep. 2015;11(6): 921–33.

[64] Gatto F, Nookaew I, Nielsen J. Chromosome 3p loss of heterozygosity is associated with a unique metabolic network in clear cell renal carcinoma. Proc Natl Acad Sci USA. 2014;111(9):E866–75.

[65] Moolamalla STR, Vinod PK. Genome-scale metabolic modelling predicts biomarkers and therapeutic targets for neuropsychiatric disorders. Comput Biol Med. 2020;125:103994.

[66] Lewis JE, Forshaw TE, Boothman DA, Furdui CM, Kemp ML. Personalized genome-scale metabolic models identify targets of redox metabolism in radiation-resistant tumors. Cell Syst. 2021;12(1):68–81.

[67] Roshanzamir F, Robinson JL, Cook D, Karimi-Jafari MH, Nielsen J. Metastatic triple negative breast cancer adapts its metabolism to destination tissues while retaining key metabolic signatures. Proc Natl Acad Sci USA. 2022;119(35):e2205456119.

[68] McGarrity S, Anuforo Ó, Halldórsson H, Bergmann A, Halldórsson S, Palsson S, et al. Metabolic systems analysis of LPS induced endothelial dysfunction applied to sepsis patient stratification. Sci Rep. 2018;8(1):6811.

[69] Shlomi T, Cabili MN, Herrgård MJ, Palsson BØ, Ruppin E. Network-based prediction of human tissue-specific metabolism. Nat Biotechnol. 2008;26(9):1003–10.

[70] Gille C, Bölling C, Hoppe A, Bulik S, Hoffmann S, Hübner K, et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Mol Syst Biol. 2010;6(1):411.

[71] Bordbar A, Feist AM, Usaite-Black R, Woodcock J, Palsson BO, Famili I. A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology. BMC Syst Biol. 2011;5:1–17.

[72] Mardinoglu A, Agren R, Kampf C, Asplund A, Nookaew I, Jacobson P, et al. Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol Syst Biol. 2013;9(1):649.

[73] Hanna EM, Zhang X, Eide M, Fallahi S, Furmanek T, Yadetie F, et al. ReCodLiver0.9: overcoming challenges in genome-scale metabolic reconstruction of a non-model species. Front Mol Biosci. 2020;7:591406.

[74] Baloni P, Sangar V, Yurkovich JT, Robinson M, Taylor S, Karbowski CM, et al. Genome-scale metabolic model of the rat liver predicts effects of diet restriction. Sci Rep. 2019;9(1):9807.

[75] Kumar A, Harrelson T, Lewis NE, Gallagher EJ, LeRoith D, Shiloach J, et al. Multi-tissue computational modeling analyzes pathophysiology of type 2 diabetes in MKR mice. PLoS One. 2014;9(7):e102319.

[76] Thiele I, Sahoo S, Heinken A, Hertel J, Heirendt L, Aurich MK, et al. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Mol Syst Biol. 2020;16(5): 1–24.

[77] Huang Z, Yoon S. Identifying metabolic features and engineering targets for productivity improvement in CHO cells by integrated transcriptomics and genome-scale metabolic model. Biochem Eng J. 2020;159:107624.

[78] Huang Z, Xu J, Yongky A, Morris CS, Polanco AL, Reily M, et al. CHO cell productivity improvement by genome-scale modeling and pathway analysis: application to feed supplements. Biochem Eng J. 2020;160:107638.

[79] Martín-Jiménez CA, Salazar-Barreto D, Barreto GE, González J. Genome-scale reconstruction of the human astrocyte metabolic network. Front Aging Neurosci. 2017;9:23.

[80] Baloni P, Funk CC, Yan J, Yurkovich JT, Kueider-Paisley A, Nho K, et al. Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimer’s disease. Cell Rep Med. 2020;1(8):100138.

[81] Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E. Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect. PLoS Comput Biol. 2011;7(3):e1002018.

[82] Folger O, Jerby L, Frezza C, Gottlieb E, Ruppin E, Shlomi T. Predicting selective drug targets in cancer through metabolic networks. Mol Syst Biol. 2011;7(1):501.

[83] Zhao H, Yang L, Baddour J, Achreja A, Bernard V, Moss T, et al. Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism. Elife. 2016;5:e10250.

[84] Massagué J, Obenauf AC. Metastatic colonization by circulating tumour cells. Nature. 2016;529(7586):298–306.

[85] ben Guebila M, Thiele I. Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes. Nat Comput Sci. 2021;1(5):348–61.

[86] Thiele I, Fleming RMT. Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication. Comput Struct Biotechnol J. 2022;20:4098–109.

[87] Cheng Y, Schlosser P, Hertel J, Sekula P, Oefner PJ, Spiekerkoetter U, et al. Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism. Nat Commun. 2021;12(1):964.

[88] Chen Y, Nielsen J. Mathematical modelling of proteome constraints within metabolism. Curr Opin Syst Biol. 2021.

[89] Dumont J, Euwart D, Mei B, Estes S, Kshirsagar R. Human cell lines for biopharmaceutical manufacturing: history, status, and future perspectives. Crit Rev Biotechnol. 2016;36(6):1110–22.

[90] Butler M, Spearman M. The choice of mammalian cell host and possibilities for glycosylation engineering. Curr Opin Biotechnol. 2014;30:107–12.

[91] Yusufi FNK, Lakshmanan M, Ho YS, Loo BLW, Ariyaratne P, Yang Y, et al. Mammalian systems biotechnology reveals global cellular adaptations in a recombinant CHO cell line. Cell Syst. 2017;4(5):530.e6–542.e6.

[92] Gutierrez JM, Feizi A, Li S, Kallehauge TB, Hefzi H, Grav LM, et al. Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion. Nat Commun. 2020;11(1):68.

[93] Vayena E, Chiappino-Pepe A, MohammadiPeyhani H, Francioli Y, Hadadi N, Ataman M, et al. A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions. Proc Natl Acad Sci USA. 2022;119(46):e2211197119.

[94] Nam H, Campodonico M, Bordbar A, Hyduke DR, Kim S, Zielinski DC, et al. A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks. PLoS Comput Biol. 2014;10(9):e1003837.

[95] Ramakrishna R, Edwards JS, McCulloch A, Palsson BO. Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints. Am J Physiol Regul Integr Comp Physiol. 2001;280(3):R695–704.

[96] Ortmayr K, Dubuis S, Zampieri M. Metabolic profiling of cancer cells reveals genome-wide crosstalk between transcriptional regulators and metabolism. Nat Commun. 2019;10:1–13.

[97] Grigaitis P, Olivier BG, Fiedler T, Teusink B, Kummer U, Veith N. Protein cost allocation explains metabolic strategies in Escherichia coli. J Biotechnol. 2021;327:54–63.

[98] Chen Y, Li F, Mao J, Chen Y, Nielsen J. Yeast optimizes metal utilization based on metabolic network and enzyme kinetics. Proc Natl Acad Sci USA. 2021;118(12):e2020154118.

[99] Abiri B, Vafa M. Iron deficiency and anemia in cancer patients: the role of iron treatment in anemic cancer patients. Nutr Cancer. 2020;72(5):864–72.

[100] Asp M, Giacomello S, Larsson L, Wu C, Fürth D, Qian X, et al. A spatiotemporal organ-wide gene expression and cell atlas of the developing human heart. Cell. 2019;179(7):1647.e19–1660.e19.

[101] Chen W-T, Lu A, Craessaerts K, Pavie B, Sala Frigerio C, Corthout N, et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell. 2020;182(4):976.e19–991.e19.

[102] Tuveson D, Clevers H. Cancer modeling meets human organoid technology. Science. 2019;364(6444):952–5.

RIGHTS & PERMISSIONS

2023 The Authors. Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.