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The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models

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Metabolic Network Reconstruction and Modeling

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

Abstract

The FASTCORE family is a family of algorithms that are mainly used to build context-specific models but can also be applied to other tasks such as gapfilling and consistency testing. The FASTCORE family has very low computational demands with running times that are several orders of magnitude lower than its main competitors. Furthermore, the models built by the FASTCORE family have a better resolution power (defined as the ability to capture metabolic variations between different tissues, cell types, or contexts) than models from other algorithms.

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References

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

    Article  PubMed  PubMed Central  Google Scholar 

  2. Pacheco MP et al (2015) Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network. BMC Genomics 16:809. https://doi.org/10.1186/s12864-015-1984-4

    Article  PubMed  PubMed Central  Google Scholar 

  3. Thiele I, Vlassis N, Fleming RMT (2014) fastGapFill: efficient gap filling in metabolic networks. Bioinformatics 30:2529–2531

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Shlomi T, Cabili MN, Herrgard MJ, Palsson BO, Ruppin E (2008) Network-based prediction of human tissue-specific metabolism. Nat Biotechnol 26:1003–1010

    Article  CAS  PubMed  Google Scholar 

  5. Schellenberger J et al (2011) Quantitative prediction of cellular metabolism with constraint-based models: the {COBRA} {T}oolbox v2.0. Nat Protoc 6:1290–1307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Steffensen JL, Dufault-Thompson K, Zhang Y (2016) PSAMM: a portable system for the analysis of metabolic models. PLoS Comput Biol 12:1–29

    Article  Google Scholar 

  7. Gautier L, Cope L, Bolstad BM, Irizarry RA (2004) affy — analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20:307–315

    Article  CAS  PubMed  Google Scholar 

  8. Carvalho BS, Irizarry RA (2010) A framework for oligonucleotide microarray preprocessing. Bioinformatics 26:2363–2367

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wicklham H (2009) ggplot2: elegant graphics for data analysis. Springer, New York, NY

    Book  Google Scholar 

  10. McCall MN, Bolstad BM, Irizarry RA (2010) Frozen robust multiarray analysis (fRMA). Biostatistics 11:242–253

    Article  PubMed  PubMed Central  Google Scholar 

  11. McCall MN, Uppal K, Jaffee HA, Zilliox MJ, Irizarry RA (2011) The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res 39:D1011–D1015

    Article  CAS  PubMed  Google Scholar 

  12. McCall MN, Irizarry RA (2016) hgu133plus2barcodevecs: hgu133plus2 data for barcode. R package version 1.10.0

    Google Scholar 

  13. Duarte NC et al (2007) Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc Natl Acad Sci 104:1777–1782

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Thiele I et al (2013) A community-driven global reconstruction of human metabolism. Nat Biotechnol 31:419

    Article  CAS  PubMed  Google Scholar 

  15. Swainston N et al (2016) Recon 2.2: from reconstruction to model of human metabolism. Metabolomics 12:109. https://doi.org/10.1007/s11306-016-1051-4

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  17. Schultz A, Qutub AA (2016) Reconstruction of Tissue-Specific Metabolic Networks Using CORDA. PLoS Comput Biol 12:e1004808

    Article  PubMed  PubMed Central  Google Scholar 

  18. Pacheco MP, Pfau T, Sauter T (2016) Benchmarking procedures for high-throughput context specific reconstruction algorithms. Front Physiol 6:410

    Article  PubMed  PubMed Central  Google Scholar 

  19. Uhlen M (2005) A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol Cell Proteomics 4:1920–1932

    Article  CAS  PubMed  Google Scholar 

  20. Chindelevitch L, Trigg J, Regev A, Berger B (2014) An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models. Nat Commun 5:4893

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We would like to thank Dr. Nikos Vlassis for his feedback.

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Correspondence to Maria Pires Pacheco .

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Pacheco, M.P., Sauter, T. (2018). The FASTCORE Family: For the Fast Reconstruction of Compact Context-Specific Metabolic Networks Models. 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_4

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  • DOI: https://doi.org/10.1007/978-1-4939-7528-0_4

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7527-3

  • Online ISBN: 978-1-4939-7528-0

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