Skip to main content

Systems Biology Approach in Hypertension Research

  • Protocol
  • First Online:
Hypertension

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1527))

Abstract

Systems biology is an approach to study all genes, gene transcripts, proteins, metabolites, and their interactions in specific cells, tissues, organs, or the whole organism. It is based on data derived from high-throughput analytical technologies and bioinformatics tools to analyze these data, and aims to understand the whole system rather than individual aspects of it. Systems biology can be applied to virtually all conditions and diseases and therefore also to hypertension and its underlying vascular disorders. Unlike other methods in this book there is no clear-cut protocol to explain a systems biology approach. We will instead outline some of the most important and common steps in the generation and analysis of systems biology data.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others

References

  1. Kirschner MW (2005) The meaning of systems biology. Cell 121:503–504

    Article  CAS  PubMed  Google Scholar 

  2. Fung MM, Zhang K, Zhang L et al (2011) Contemporary approaches to genetic influences on hypertension. Curr Opin Nephrol Hypertens 20:23–30

    Article  PubMed  Google Scholar 

  3. Thongboonkerd V (2005) Genomics, proteomics and integrative “omics” in hypertension research. Curr Opin Nephrol Hypertens 14:133–139

    Article  CAS  PubMed  Google Scholar 

  4. Gerszten RE, Wang TJ (2008) The search for new cardiovascular biomarkers. Nature 451:949–952

    Article  CAS  PubMed  Google Scholar 

  5. Bindea G, Mlecnik B, Hackl H et al (2009) ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25:1091–1093

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Smoot ME, Ono K, Ruscheinski J et al (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432

    Article  CAS  PubMed  Google Scholar 

  7. Flicek P, Ahmed I, Amode MR et al (2012) Ensembl 2013. Nucleic Acids Res 41:D48–D55

    Google Scholar 

  8. Seal RL, Gordon SM, Lush MJ et al (2011) genenames.org: the HGNC resources in 2011. Nucleic Acids Res 39:D514–D519

    Article  CAS  PubMed  Google Scholar 

  9. Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Razick S, Magklaras G, Donaldson IM (2008) iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9:405

    Article  PubMed  PubMed Central  Google Scholar 

  11. Kanehisa M, Goto S, Sato Y et al (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114

    Article  CAS  PubMed  Google Scholar 

  12. Perkins DN, Pappin DJ, Creasy DM et al (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20:3551–3567

    Article  CAS  PubMed  Google Scholar 

  13. Tarcea VG, Weymouth T, Ade A et al (2009) Michigan molecular interactions r2: from interacting proteins to pathways. Nucleic Acids Res 37:D642–D646

    Article  CAS  PubMed  Google Scholar 

  14. Kwon T, Choi H, Vogel C et al (2011) MSblender: a probabilistic approach for integrating peptide identifications from multiple database search engines. J Proteome Res 10:2949–2958

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Geer LY, Markey SP, Kowalak JA et al (2004) Open mass spectrometry search algorithm. J Proteome Res 3:958–964

    Article  CAS  PubMed  Google Scholar 

  16. van Iersel MP, Kelder T, Pico AR et al (2008) Presenting and exploring biological pathways with PathVisio. BMC Bioinformatics 9:399

    Article  PubMed  PubMed Central  Google Scholar 

  17. Croft D, O'Kelly G, Wu G et al (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 39:D691–D697

    Article  CAS  PubMed  Google Scholar 

  18. UniProt Consortium (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res 40:D71–D75

    Google Scholar 

  19. Husi H, Van Agtmael T, Mullen W, Bahlmann FH, Schanstra JP, Vlahou A, Delles C, Perco P, Mischak H (2014) Proteome-based systems biology analysis of the diabetic mouse aorta reveals major changes in fatty acid biosynthesis as potential hallmark in diabetes mellitus-associated vascular disease. Circ Cardiovasc Genet. 7(2):161–70. doi:10.1161/CIRCGENETICS.113.000196.

Download references

Acknowledgments

Work in our laboratory is supported by grants from the European Union (“EU-MASCARA,” grant agreement 278249; and “PRIORITY,” grant agreement 279277) and by the Scottish Government (Strategic Research Development Grant “Biomarkers for Battling Chronic Disease”).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christian Delles .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media LLC

About this protocol

Cite this protocol

Delles, C., Husi, H. (2017). Systems Biology Approach in Hypertension Research. In: Touyz, R., Schiffrin, E. (eds) Hypertension. Methods in Molecular Biology, vol 1527. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6625-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6625-7_6

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6623-3

  • Online ISBN: 978-1-4939-6625-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics