Skip to main content

A Critical Evaluation of Methods for the Reconstruction of Tissue-Specific Models

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

Included in the following conference series:

Abstract

Under the framework of constraint based modeling, genome-scale metabolic models (GSMMs) have been used for several tasks, such as metabolic engineering and phenotype prediction. More recently, their application in health related research has spanned drug discovery, biomarker identification and host-pathogen interactions, targeting diseases such as cancer, Alzheimer, obesity or diabetes. In the last years, the development of novel techniques for genome sequencing and other high-throughput methods, together with advances in Bioinformatics, allowed the reconstruction of GSMMs for human cells. Considering the diversity of cell types and tissues present in the human body, it is imperative to develop tissue-specific metabolic models. Methods to automatically generate these models, based on generic human metabolic models and a plethora of omics data, have been proposed. However, their results have not yet been adequately and critically evaluated and compared.

This work presents a survey of the most important tissue or cell type specific metabolic model reconstruction methods, which use literature, transcriptomics, proteomics and metabolomics data, together with a global template model. As a case study, we analyzed the consistency between several omics data sources and reconstructed distinct metabolic models of hepatocytes using different methods and data sources as inputs. The results show that omics data sources have a poor overlapping and, in some cases, are even contradictory. Additionally, the hepatocyte metabolic models generated are in many cases not able to perform metabolic functions known to be present in the liver tissue. We conclude that reliable methods for a priori omics data integration are required to support the reconstruction of complex models of human cells.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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 Computational Biology 8(5), e1002518 (2012)

    Article  Google Scholar 

  2. 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. Molecular Systems Biology 10, 721 (2014)

    Article  Google Scholar 

  3. Barrett, T., Troup, D.B., Wilhite, S.E., Ledoux, P., et al.: NCBI GEO: archive for functional genomics data sets - 10 years on. Nucleic Acids Research 39(suppl 1), D1005–D1010 (2011)

    Article  Google Scholar 

  4. Carlson, M.: hgu133plus2.db: Affymetrix Human Genome U133 Plus 2.0 Array annotation data (chip hgu133plus2) (2014). r package version 3.0.0

    Google Scholar 

  5. Duarte, N.C., Becker, S.A., Jamshidi, N., Thiele, I., Mo, M.L., Vo, T.D., Srivas, R., Palsson, B.O.: Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proceedings of the National Academy of Sciences of the United States of America 104(6), 1777–1782 (2007)

    Article  Google Scholar 

  6. Duarte, N.C., Herrgård, M.J., Palsson, B.O.: Reconstruction and validation of Saccharomyces cerevisiae iND750, a fully compartmentalized genome-scale metabolic model. Genome Research 14(7), 1298–1309 (2004)

    Article  Google Scholar 

  7. Flicek, P., Amode, M.R., Barrell, D., et al.: Ensembl 2014. Nucleic Acids Research 42(D1), D749–D755 (2014)

    Article  Google Scholar 

  8. Gille, C., Bölling, C., Hoppe, A., et al.: HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Molecular Systems Biology 6(411), 411 (2010)

    Google Scholar 

  9. Hao, T., Ma, H.W., Zhao, X.M., Goryanin, I.: Compartmentalization of the Edinburgh Human Metabolic Network. BMC Bioinformatics 11, 393 (2010)

    Article  Google Scholar 

  10. Ishibashi, H., Nakamura, M., Komori, A., Migita, K., Shimoda, S.: Liver architecture, cell function, and disease. Seminars in Immunopathology 31(3) (2009)

    Google Scholar 

  11. Jerby, L., Ruppin, E.: Predicting Drug Targets and Biomarkers of Cancer via Genome-Scale Metabolic Modeling. Clinical Cancer Research : An Official Journal of the American Association for Cancer Research 18(20), 5572–5584 (2012)

    Article  Google Scholar 

  12. Jerby, L., Shlomi, T., Ruppin, E.: Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Molecular Systems Biology 6(401), 401 (2010)

    Google Scholar 

  13. Kaddurah-Daouk, R., Kristal, B., Weinshilboum, R.: Metabolomics: a global biochemical approach to drug response and disease. Annu. Rev. Pharmacol. Toxicol. 48, 653–683 (2008)

    Article  Google Scholar 

  14. Lewis, N.E., Schramm, G., Bordbar, A., Schellenberger, J., Andersen, M.P., Cheng, J.K., Patel, N., Yee, A., Lewis, R.A., Eils, R., König, R., Palsson, B.O.: Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nature Biotechnology 28(12), 1279–1285 (2010)

    Article  Google Scholar 

  15. 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. Nature Communications 5, Jan 2014

    Google Scholar 

  16. McCall, M.N., Jaffee, H.A., Zelisko, S.J., Sinha, N., et al.: The Gene Expression Barcode 3.0: improved data processing and mining tools. Nucleic Acids Research 42(D1), D938–D943 (2014)

    Article  Google Scholar 

  17. Oberhardt, M.A., Palsson, B.O., Papin, J.A.: Applications of genome-scale metabolic reconstructions. Molecular Systems Biology 5(320), 320 (2009)

    Google Scholar 

  18. Orth, J.D., Thiele, I., Palsson, B.O.: What is flux balance analysis? Nature Biotechnology 28(3), 245–248 (2010)

    Article  Google Scholar 

  19. Parkinson, H., Sarkans, U., Shojatalab, M., Abeygunawardena, N., et al.: ArrayExpress-a public repository for microarray gene expression data at the EBI. Nucleic Acids Research 33(Database issue), Jan 2005

    Google Scholar 

  20. Reed, J.L., Vo, T.D., Schilling, C.H., Palsson, B.O.: An expanded genome-scale model of Escherichia coli K-12 ( i JR904 GSM / GPR ) 4(9), 1–12 (2003)

    Google Scholar 

  21. Sahoo, S., Franzson, L., Jonsson, J.J., Thiele, I.: A compendium of inborn errors of metabolism mapped onto the human metabolic network. Mol. BioSyst. 8(10), 2545–2558 (2012)

    Article  Google Scholar 

  22. Sahoo, S., Thiele, I.: Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells. Human Molecular Genetics 22(13), 2705–2722 (2013)

    Article  Google Scholar 

  23. 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 Computational Biology 7(3), e1002018 (2011)

    Article  Google Scholar 

  24. Shlomi, T., Cabili, M.N., Ruppin, E.: Predicting metabolic biomarkers of human inborn errors of metabolism. Molecular Systems Biology 5(263), 263 (2009)

    Google Scholar 

  25. Thiele, I., Swainston, N., Fleming, R.M.T., et al.: A community-driven global reconstruction of human metabolism. Nature Biotechnology 31(5), May 2013

    Google Scholar 

  26. Uhlen, M., Oksvold, P., Fagerberg, L., Lundberg, E., et al.: Towards a knowledge-based Human Protein Atlas. Nat Biotech 28(12), 1248–1250 (2010)

    Article  Google Scholar 

  27. Wang, Y., Eddy, J.A., Price, N.D.: Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE. BMC Systems Biology 6(1), 153 (2012)

    Article  Google Scholar 

  28. Wishart, D.S., Knox, C., Guo, A.C., Eisner, R., et al.: HMDB: a knowledgebase for the human metabolome. Nucleic Acids Research 37(suppl 1), Jan 2009

    Google Scholar 

  29. Yizhak, K., Le Dévédec, S.E., Rogkoti, V.M.M., et al.: A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Molecular Systems Biology 10(8) (2014)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Miguel Rocha .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Correia, S., Rocha, M. (2015). A Critical Evaluation of Methods for the Reconstruction of Tissue-Specific Models. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23485-4_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics