Skip to main content
SpringerLink
Log in

Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives

  • Protocol
  • First Online:
Metabolic Network Reconstruction and Modeling

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1716))

Abstract

Genome-scale metabolic models are valuable tools for assessing the metabolic potential of living organisms. Being downstream of gene expression, metabolism is increasingly being used as an indicator of the phenotypic outcome for drugs and therapies. We here present a review of the principal methods used for constraint-based modelling in systems biology, and explore how the integration of multi-omic data can be used to improve phenotypic predictions of genome-scale metabolic models. We believe that the large-scale comparison of the metabolic response of an organism to different environmental conditions will be an important challenge for genome-scale models. Therefore, within the context of multi-omic methods, we describe a tutorial for multi-objective optimization using the metabolic and transcriptomics adaptation estimator (METRADE), implemented in MATLAB. METRADE uses microarray and codon usage data to model bacterial metabolic response to environmental conditions (e.g., antibiotics, temperatures, heat shock). Finally, we discuss key considerations for the integration of multi-omic networks into metabolic models, towards automatically extracting knowledge from such models.

Supreeta Vijayakumar and Max Conway contributed equally to this work.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Louca S, Doebeli M (2015) Calibration and analysis of genome-based models for microbial ecology. Elife 4:e08208

    Article  PubMed  PubMed Central  Google Scholar 

  2. Nilsson A, Nielsen J (2016) Genome scale metabolic modeling of cancer. Metab Eng 43(B):103–112

    Google Scholar 

  3. Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28(3):245–248

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zieliński ŁP, Smith AC, Smith AG, Robinson AJ (2016) Metabolic flexibility of mitochondrial respiratory chain disorders predicted by computer modelling. Mitochondrion 31:45–55

    Article  PubMed  PubMed Central  Google Scholar 

  5. Palsson BØ (2011) Systems biology: simulation of dynamic network states. Cambridge University Press, Cambridge

    Book  Google Scholar 

  6. Jayaraman A, Hahn J (2009) Methods in Bioengineering: systems analysis of biological networks. Artech House methods in bioengineering series. Artech House, Boston. https://books.google.co.uk/books?id=Haod3KR-tR8C

    Google Scholar 

  7. Bordbar A, Monk JM, King ZA, Palsson BO (2014) Constraint-based models predict metabolic and associated cellular functions. Nat Rev Genet 15(2):107–120

    Article  CAS  PubMed  Google Scholar 

  8. Burgard AP, Vaidyaraman S, Maranas CD (2001) Minimal reaction sets for Escherichia coli metabolism under different growth requirements and uptake environments. Biotechnol Prog 17(5):791–797

    Article  CAS  PubMed  Google Scholar 

  9. Mahadevan R, Schilling C (2003) The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng 5(4):264–276

    Article  CAS  PubMed  Google Scholar 

  10. Mahadevan R, Edwards JS, Doyle FJ (2002) Dynamic flux balance analysis of diauxic growth in Escherichia coli. Biophys J 83(3):1331–1340

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2016) Kegg: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. http://dx.doi.org/10.1093/nar/gkw1092

  12. King ZA, Lu J, Dräger A, Miller P, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, Lewis NE (2016) Bigg models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44(D1):D515–D522

    Article  CAS  PubMed  Google Scholar 

  13. Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Mueller LA et al (2016) The metacyc database of metabolic pathways and enzymes and the biocyc collection of pathway/genome databases. Nucleic Acids Res 44(D1):D471–D480

    Article  CAS  PubMed  Google Scholar 

  14. Devoid S, Overbeek R, DeJongh M, Vonstein V, Best AA, Henry C (2013) Automated genome annotation and metabolic model reconstruction in the seed and model seed. In: Systems metabolic engineering: methods and protocols. Humana Press, New York, pp 17–45

    Chapter  Google Scholar 

  15. Angione C, Pratanwanich N, Lió P (2015) A hybrid of metabolic flux analysis and bayesian factor modeling for multiomic temporal pathway activation. ACS Synth Biol 4(8):880–889

    Article  CAS  PubMed  Google Scholar 

  16. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, Adkins JN, Schramm G, Purvine SO, Lopez-Ferrer D et al (2010) Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol 6(1):390

    Google Scholar 

  17. Palsson B (2015) Systems biology: constraint-based reconstruction and analysis. Cambridge University Press, Cambridge. https://books.google.co.uk/books?id=QNBpBgAAQBAJ

    Book  Google Scholar 

  18. Voigt C (2011) Synthetic biology, part b: computer aided design and DNA assembly. Methods in enzymology. Elsevier Science, Amsterdam. https://books.google.co.uk/books?id=9uPvZWiabr4C

    Google Scholar 

  19. Deutscher D, Meilijson I, Schuster S, Ruppin E (2008) Can single knockouts accurately single out gene functions? BMC Syst Biol 2(1):50

    Article  PubMed  PubMed Central  Google Scholar 

  20. Shlomi T, Berkman O, Ruppin E (2005) Regulatory on/off minimization of metabolic flux changes after genetic perturbations. Proc Natl Acad Sci USA 102(21):7695–7700

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Megchelenbrink W, Katzir R, Lu X, Ruppin E, Notebaart RA (2015) Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival. Proc Natl Acad Sci 112(39):12217–12222

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Conway M, Angione C, Liò P (2016) Iterative multi level calibration of metabolic networks. Curr Bioinforma 11(1):93–105

    Article  CAS  Google Scholar 

  23. Costanza J, Carapezza G, Angione C, Lió P, Nicosia G (2012) Robust design of microbial strains. Bioinformatics 28(23):3097–3104

    Article  CAS  PubMed  Google Scholar 

  24. Angione C, Lió P (2015) Predictive analytics of environmental adaptability in multi-omic network models. Sci Rep 5:15147

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Angione C, Costanza J, Carapezza G, Lió P, Nicosia G (2015) Multi-target analysis and design of mitochondrial metabolism. PloS One 10(9):e0133825

    Article  PubMed  PubMed Central  Google Scholar 

  26. Xu G (2011) An iterative strategy for bi-objective optimization of metabolic pathways. In: 2011 fourth international joint conference on computational sciences and optimization

    Google Scholar 

  27. Sendin J, Exler O, Banga JR (2010) Multi-objective mixed integer strategy for the optimisation of biological networks. IET Syst Biol 4(3):236–248

    Article  CAS  PubMed  Google Scholar 

  28. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. http://dx.doi.org/10.1109/4235.996017

    Article  Google Scholar 

  29. Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol 3(4):257–271

    Google Scholar 

  30. Zhang Q, Li H (2007) Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  Google Scholar 

  31. Nagrath D, Avila-Elchiver M, Berthiaume F, Tilles AW, Messac A, Yarmush ML (2010) Soft constraints-based multiobjective framework for flux balance analysis. Metab Eng 12(5):429–445

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kelk SM, Olivier BG, Stougie L, Bruggeman FJ (2012) Optimal flux spaces of genome-scale stoichiometric models are determined by a few subnetworks. Sci Rep 2:580

    Article  PubMed  PubMed Central  Google Scholar 

  33. Maarleveld TR, Wortel MT, Olivier BG, Teusink B, Bruggeman FJ (2015) Interplay between constraints, objectives, and optimality for genome-scale stoichiometric models. PLoS Comput Biol 11(4):e1004166

    Article  PubMed  PubMed Central  Google Scholar 

  34. Oh YG, Lee DY, Lee SY, Park S (2009) Multiobjective flux balancing using the NISE method for metabolic network analysis. Biotechnol Prog 25(4):999–1008

    Article  CAS  PubMed  Google Scholar 

  35. Budinich M, Bourdon J, Larhlimi A, Eveillard D (2017) A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems. PloS One 12(2):e0171744

    Article  PubMed  PubMed Central  Google Scholar 

  36. John PCS, Crowley MF, Bomble YJ (2016) Efficient estimation of the maximum metabolic productivity of batch systems. arXiv preprint. arXiv:161001114

    Google Scholar 

  37. Machado D, Herrgård M (2014) Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol 10(4):e1003580

    Article  PubMed  PubMed Central  Google Scholar 

  38. Covert MW, Schilling CH, Palsson B (2001) Regulation of gene expression in flux balance models of metabolism. J Theor Biol 213(1):73–88

    Article  CAS  PubMed  Google Scholar 

  39. Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci 107(41):17845–17850

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chandrasekaran S, Price ND (2013) Metabolic constraint-based refinement of transcriptional regulatory networks. PLoS Comput Biol 9(12):e1003370

    Article  PubMed  PubMed Central  Google Scholar 

  41. Rügen M, Bockmayr A, Steuer R (2015) Elucidating temporal resource allocation and diurnal dynamics in phototrophic metabolism using conditional FBA. Sci Rep 5, 15247

    Article  PubMed  PubMed Central  Google Scholar 

  42. Reimers AM, Knoop H, Bockmayr A, Steuer R (2016) Evaluating the stoichiometric and energetic constraints of cyanobacterial diurnal growth. arXiv preprint. arXiv:161006859

    Google Scholar 

  43. Angione C, Conway M, Lió P (2016) Multiplex methods provide effective integration of multi-omic data in genome-scale models. BMC Bioinf 17(4):83

    Article  Google Scholar 

  44. Ebrahim A, Brunk E, Tan J, O’Brien EJ, Kim D, Szubin R, Lerman JA, Lechner A, Sastry A, Bordbar A et al (2016) Multi-omic data integration enables discovery of hidden biological regularities. Nat Commun 71:13091

    Google Scholar 

  45. Segre D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci 99(23):15112–15117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Raval A, Ray A (2013) Introduction to biological networks. CRC Press, Boca Raton

    Google Scholar 

  47. Machado D, Costa RS, Ferreira EC, Rocha I, Tidor B (2012) Exploring the gap between dynamic and constraint-based models of metabolism. Metab Eng 14(2):112–119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Brochado AR, Andrejev S, Maranas CD, Patil KR (2012) Impact of stoichiometry representation on simulation of genotype-phenotype relationships in metabolic networks. PLoS Comput Biol 8(11):e1002758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yizhak K, Benyamini T, Liebermeister W, Ruppin E, Shlomi T (2010) Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 26(12):i255–i260

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Monk JM, Koza A, Campodonico MA, Machado D, Seoane JM, Palsson BO, Herrgård MJ, Feist AM (2016) Multi-omics quantification of species variation of Escherichia coli links molecular features with strain phenotypes. Cell Syst 3(3):238–251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Vivek-Ananth R, Samal A (2016) Advances in the integration of transcriptional regulatory information into genome-scale metabolic models. Biosystems 147:1–10

    Google Scholar 

  52. Becker SA, Palsson BØ (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 4(5):e1000082

    Article  PubMed  PubMed Central  Google Scholar 

  53. Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE (2009) Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol 5(8):e1000489

    Article  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  55. Vlassis N, Pacheco MP, Sauter T (2014) Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol 10(1):e1003424

    Article  PubMed  PubMed Central  Google Scholar 

  56. Wang Y, Eddy JA, Price ND (2012) Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Syst Biol 6(1):153

    Article  PubMed  PubMed Central  Google Scholar 

  57. Imam S, Schäuble S, Brooks AN, Baliga NS, Price ND (2015) Data-driven integration of genome-scale regulatory and metabolic network models. Front Microbiol 6:409

    Article  PubMed  PubMed Central  Google Scholar 

  58. Carrera J, Estrela R, Luo J, Rai N, Tsoukalas A, Tagkopoulos I (2014) An integrative, multi-scale, genome-wide model reveals the phenotypic landscape of Escherichia coli. Mol Syst Biol 10(7):735

    Article  PubMed  PubMed Central  Google Scholar 

  59. Kashaf SS, Angione C, Lió P (2017) Making life difficult for clostridium difficile: augmenting the pathogen’s metabolic model with transcriptomic and codon usage data for better therapeutic target characterization. BMC Syst Biol 11(1):25

    Article  PubMed  PubMed Central  Google Scholar 

  60. Zitzler E, Brockhoff D, Thiele L (2007) The hypervolume indicator revisited: on the design of pareto-compliant indicators via weighted integration. In: International conference on evolutionary multi-criterion optimization. Springer, Berlin, pp 862–876

    Chapter  Google Scholar 

  61. Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26(3):303

    Article  PubMed  Google Scholar 

  62. Trefethen LN, Embree M (2005) Spectra and pseudospectra: the behavior of nonnormal matrices and operators. Princeton University Press, Princeton

    Google Scholar 

  63. Newman M (2013) Spectral community detection in sparse networks. arXiv preprint. arXiv:13086494

    Google Scholar 

  64. Schellenberger J, Que R, Fleming RM, Thiele I, Orth JD, Feist AM, Zielinski DC, Bordbar A, Lewis NE, Rahmanian S et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the cobra toolbox v2. 0. Nat Protoc 6(9):1290–1307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Orth J, Conrad T, Na J, Lerman J, Nam H, Feist A, Palsson B (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism. Mol Syst Biol 7(1):535

    Article  PubMed  PubMed Central  Google Scholar 

  66. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8

    Article  PubMed  PubMed Central  Google Scholar 

  67. Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, Hwa T, Williamson JR (2015) Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol Syst Biol 11(2):784

    Article  PubMed  PubMed Central  Google Scholar 

  68. Li C, Li H (2008) Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics 24(9):1175–1182

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Systems, Teesside University, Middlesbrough, Tees Valley TS1 3BX, UK

    Supreeta Vijayakumar & Claudio Angione

  2. Computer Laboratory, University of Cambridge, Cambridge, UK

    Max Conway & Pietro Lió

Authors
  1. Supreeta Vijayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Max Conway
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pietro Lió
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Claudio Angione
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Claudio Angione .

Editor information

Editors and Affiliations

  1. Department of Biology, University of Florence, Sesto Fiorentino, Florence, Italy

    Marco Fondi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Vijayakumar, S., Conway, M., Lió, P., Angione, C. (2018). Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives. In: Fondi, M. (eds) Metabolic Network Reconstruction and Modeling. Methods in Molecular Biology, vol 1716. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7528-0_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7528-0_18

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7527-3

  • Online ISBN: 978-1-4939-7528-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation