Skip to main content

Systems Biology: Generating and Understanding Big Data

  • Chapter
  • First Online:
Book cover Success in Academic Surgery: Basic Science

Part of the book series: Success in Academic Surgery ((SIAS))

  • 684 Accesses

Abstract

Systems biology is the study of complex biological systems from a holistic, big-picture view. Advancement in biological research techniques to generate more data efficiently has facilitated a surge in systems biology, which relies on analysis of large datasets to elucidate a cell’s genome, transcriptome, proteome, and metabolome. Large biological datasets are generated from high-throughput experiments, such as microarrays, mass spectrometry, and high-throughput drug screening. Many datasets from previous experiments done by various laboratories and organizations are available in numerous online portals and can provide valuable information. Analysis of data from genomic, transcriptomic, proteomic, and metabolomic experiments can elucidate changes caused by perturbations like disease process and therapeutic interventions. Although each type of “omics” dataset on its own can provide important insights, integrating data from multiple omics experiments and dimensions (e.g., genome and proteome) can provide a better understanding of how different dimensions of biology are coordinated with each other. This can lead to comprehensive information on causes and effects of a disease process and effectiveness and resistance to therapies.

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 EPUB and 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

References

  1. Westerhoff HV, Pallsson BO. The evolution of molecular biology into systems biology. Nat Biotechnol. 2004;22(10):1249–52.

    Article  CAS  Google Scholar 

  2. Lander ES. Initial impact of the sequencing of the human genome. Nature. 2011;470(7333):187–97.

    Article  CAS  Google Scholar 

  3. Ayers D, Day PJ. Systems medicine: the application of systems biology approaches for modern medical research and drug development. Mol Biol Int. 2015;2015:698169.

    Article  Google Scholar 

  4. The 1000 Genomes Project Consortium, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74.

    Article  Google Scholar 

  5. Kolesnikov N, et al. ArrayExpress update-simplifying data submissions. Nucleic Acids Res. 2015;43(Database issue):D1113–6.

    Article  CAS  Google Scholar 

  6. Sherry ST, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–11.

    Article  CAS  Google Scholar 

  7. Barrett T, Se W, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013. Jan;4:991–5.

    Google Scholar 

  8. Krupp M, Marquardt JU, Sahin U, et al. RNA-Seq Atlas—a reference database for gene expression profiling in normal tissue by next-generation sequencing. Bioinformatics. 2012;8:1184–5.

    Article  Google Scholar 

  9. Peri S, Navarro JD, Kristiansen TZ, et al. Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 2004;32:D497–501.

    Article  CAS  Google Scholar 

  10. Hermjakob H, Montecchi-Palazzi L, Lewington C, et al. IntAct: an open source molecular interaction database. Nucleic Acids Res. 2004;32:D42–455.

    Article  Google Scholar 

  11. Hulo N, Bairoch A, Bulliard V, et al. The 20 years of PROSITE. Nucleic Acids Res. 2008;36:D245–9.

    Article  CAS  Google Scholar 

  12. Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucleic Acids Res. 2000;28(1):235–42.

    Article  CAS  Google Scholar 

  13. The Uniprot Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2017;45:158–69.

    Article  Google Scholar 

  14. Thul PJ, Lindskog C. The human protein atlas: a spatial map of the human proteome. Protein Sci. 2018;27(1):233–44.

    Article  CAS  Google Scholar 

  15. Guijas C, Montenegro-Burke JR, Domingo-Almenara X, et al. METLIN: a technology platform for identifying knowns and unknowns. Anal Chem. 2018;90(5):3156–64.

    Article  CAS  Google Scholar 

  16. Frolkis A, Knox C, Lim E, et al. SMPDB: the small molecule pathway database. Nucleic Acid Res. 2010;38:D480–7.

    Article  CAS  Google Scholar 

  17. Wishart DS, Tzur D, Knox C, et al. HMDB: the human metabolome database. Nucleic Acid Res. 2007;35:D521–6.

    Article  CAS  Google Scholar 

  18. Artimo P, Jonnalagedda M, Arnold K, et al. ExPASy: SIB bioinformatics resource portal. Nucleic Acids Res. 2012;40(Web Server issue):W597–603.

    Article  CAS  Google Scholar 

  19. Kanehisa M, Goto S, Sato Y, et al. Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 2014;42:D199–205.

    Article  CAS  Google Scholar 

  20. Gaulton A, Bellis LJ, Bento AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(Database issue):D1100–7.

    Article  CAS  Google Scholar 

  21. Wishart DS, Knox C, Guo AC, et al. DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 2008;36(Database issue):D901–6.

    Article  CAS  Google Scholar 

  22. Amberger JS, Bocchini CA, Schiettecatte F, et al. OMIM.org: online Mendelian inheritance in man (OMIM), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43(Database issue):D789–98.

    Article  Google Scholar 

  23. Metzker ML. Sequencing technologies – the next generation. Nat Rev Genet. 2010;11(1):31–46.

    Article  CAS  Google Scholar 

  24. Kumar RM. The widely used diagnostics of “DNA microarray” – a review. Am J Infect Dis. 2009;5(3):207–18.

    Article  CAS  Google Scholar 

  25. Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell. 2017;32(2):185–203.

    Article  Google Scholar 

  26. Downard K. Mass spectrometry’s beginnings. In: Downard K, editor. Mass spectrometry: a foundation course. London: Royal Society of Chemistry; 2004. p. 1–9.

    Google Scholar 

  27. Key M. A tutorial in displaying mass-spectrometry-based proteomic data using heat maps. BMC Bioinformatics. 2012;13(Suppl 16):S10.

    Article  CAS  Google Scholar 

  28. Altaf-Ul-Amin MD, Afendi FM, Kiboi SK, et al. Systems biology in the context of big data and networks. Biomed Res Int. 2014;2014:428570.

    PubMed  PubMed Central  Google Scholar 

  29. Dunkler D, Sanchez-Cabo F, Heinze G. Statistical analysis principles for omics data. Methods Mol Biol. 2011;719:113–31.

    Article  CAS  Google Scholar 

  30. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biology. 2017;18(1):83.

    Article  Google Scholar 

  31. Jha AK, Huang SC, Sergushichev A, et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity. 2015;42(3):419–30.

    Article  CAS  Google Scholar 

  32. Markossian S, Ang KK, Wilson CG, et al. Small-molecule screening for genetic diseases. Annu Rev Genomics Hum Genet. 2018;19:263–88.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Timothy R. Donahue .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kim, S.S., Donahue, T.R. (2019). Systems Biology: Generating and Understanding Big Data. In: Kennedy, G., Gosain, A., Kibbe, M., LeMaire, S. (eds) Success in Academic Surgery: Basic Science. Success in Academic Surgery. Springer, Cham. https://doi.org/10.1007/978-3-030-14644-3_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-14644-3_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-14643-6

  • Online ISBN: 978-3-030-14644-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics