Display options
Cite
Engelhardt B, Frőhlich H, Kschischo M. Learning (from) the errors of a systems biology model. Sci Rep. 2016;6:20772doi: 10.1038/srep20772.
Engelhardt, B., Frőhlich, H., & Kschischo, M. (2016). Learning (from) the errors of a systems biology model. Scientific reports, 620772. https://doi.org/10.1038/srep20772
Engelhardt, Benjamin, et al. "Learning (from) the errors of a systems biology model." Scientific reports vol. 6 (2016): 20772. doi: https://doi.org/10.1038/srep20772
Engelhardt B, Frőhlich H, Kschischo M. Learning (from) the errors of a systems biology model. Sci Rep. 2016 Feb 11;6:20772. doi: 10.1038/srep20772. PMID: 26865316; PMCID: PMC4749970.
Copy Download .nbib Format: NLM
Share it on

Sci Rep. 2016 Feb 11;6:20772. doi: 10.1038/srep20772.

Learning (from) the errors of a systems biology model.

Scientific reports

Benjamin Engelhardt, Holger Frőhlich, Maik Kschischo

Affiliations

  1. Rheinische Friedrich-Wilhelms-Universität Bonn, Institute for Computer Science, Algorithmic Bioinformatics, c/o Bonn-Aachen International Center for IT, Dahlmannstr. 2, 53113, Bonn, Germany.
  2. University of Applied Sciences Koblenz, RheinAhrCampus, Department of Mathematics and Technology, Joseph-Rovan-Allee 2, 53424 Remagen, Germany.

PMID: 26865316 PMCID: PMC4749970 DOI: 10.1038/srep20772

Abstract

Mathematical modelling is a labour intensive process involving several iterations of testing on real data and manual model modifications. In biology, the domain knowledge guiding model development is in many cases itself incomplete and uncertain. A major problem in this context is that biological systems are open. Missed or unknown external influences as well as erroneous interactions in the model could thus lead to severely misleading results. Here we introduce the dynamic elastic-net, a data driven mathematical method which automatically detects such model errors in ordinary differential equation (ODE) models. We demonstrate for real and simulated data, how the dynamic elastic-net approach can be used to automatically (i) reconstruct the error signal, (ii) identify the target variables of model error, and (iii) reconstruct the true system state even for incomplete or preliminary models. Our work provides a systematic computational method facilitating modelling of open biological systems under uncertain knowledge.

References

  1. Proc Natl Acad Sci U S A. 2003 Feb 4;100(3):1028-33 - PubMed
  2. Proc Natl Acad Sci U S A. 2014 Dec 30;111(52):18507-12 - PubMed
  3. Bioinformatics. 2009 Aug 1;25(15):1923-9 - PubMed
  4. BMC Syst Biol. 2010;4:11 - PubMed
  5. BMC Syst Biol. 2010;4:92 - PubMed
  6. Chaos. 2010 Dec;20(4):045105 - PubMed
  7. Nat Methods. 2011 Feb;8(2):130-1 - PubMed
  8. PLoS Comput Biol. 2012;8(6):e1002548 - PubMed
  9. Bioinformatics. 2012 Sep 15;28(18):i529-i534 - PubMed
  10. Proc Natl Acad Sci U S A. 2013 Feb 12;110(7):2460-5 - PubMed
  11. Sci Signal. 2013 May 28;6(277):ra41 - PubMed
  12. BMC Syst Biol. 2013;7:76 - PubMed
  13. PLoS Comput Biol. 2013;9(9):e1003204 - PubMed
  14. BMC Syst Biol. 2014;8:1 - PubMed
  15. Bioinformatics. 2014 Jan 15;30(2):221-7 - PubMed
  16. BMC Biol. 2014;12:29 - PubMed
  17. Proc Natl Acad Sci U S A. 2014 Aug 5;111(31):11539-44 - PubMed
  18. Mol Genet Genomics. 2014 Oct;289(5):727-34 - PubMed
  19. Science. 1950 Jan 13;111(2872):23-9 - PubMed

MeSH terms

Publication Types