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Open AccessCommentary

Personalized Computational Models as Biomarkers

by Walter Kolch 1,2,3,* and Dirk Fey 1,*
1
Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
2
Conway Institute of Biomolecular & Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland
3
School of Medicine, University College Dublin, Belfield, Dublin 4, Ireland
*
Authors to whom correspondence should be addressed.
Academic Editor: Stephen B. Liggett
J. Pers. Med. 2017, 7(3), 9; https://doi.org/10.3390/jpm7030009
Received: 20 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
Biomarkers are cornerstones of clinical medicine, and personalized medicine, in particular, is highly dependent on reliable and highly accurate biomarkers for individualized diagnosis and treatment choice. Modern omics technologies, such as genome sequencing, allow molecular profiling of individual patients with unprecedented resolution, but biomarkers based on these technologies often lack the dynamic element to follow the progression of a disease or response to therapy. Here, we discuss computational models as a new conceptual approach to biomarker discovery and design. Being able to integrate a large amount of information, including dynamic information, computational models can simulate disease evolution and response to therapy with high sensitivity and specificity. By populating these models with personal data, they can be highly individualized and will provide a powerful new tool in the armory of personalized medicine. View Full-Text
Keywords: biomarkers; neuroblastoma; mathematical/computational modelling; personalized medicine biomarkers; neuroblastoma; mathematical/computational modelling; personalized medicine
MDPI and ACS Style

Kolch, W.; Fey, D. Personalized Computational Models as Biomarkers. J. Pers. Med. 2017, 7, 9.

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