Better explicating the strengths and shortcomings of these models will help refine projections and improve transparency in the years ahead.
Image credit: Witsawat.S.
Edited* by Dave Higdon, Los Alamos National Laboratory, and accepted by the Editorial Board March 24, 2011 (received for review October 22, 2010)
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History:
- Published in issue May 24, 2011.
- Published first May 6, 2011.
Author Information
- aDivision of Molecular Biosciences, Imperial College London, London SW7 2AZ, United Kingdom;
- bSystems Biology Centre, University of Warwick, Coventry CV4 7AL, United Kingdom; and
- cMathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom
Edited* by Dave Higdon, Los Alamos National Laboratory, and accepted by the Editorial Board March 24, 2011 (received for review October 22, 2010)
Footnotes
- 1To whom correspondence may be addressed. E-mail: m.komorowski{at}imperial.ac.uk or m.stumpf{at}imperial.ac.uk.
Author contributions: M.K., D.A.R., and M.P.H.S. designed research; M.K. and M.J.C. performed research; M.K. and M.P.H..S. analyzed data; and M.K. and M.P.H.S. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission. D.H. is a guest editor invited by the Editorial Board.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1015814108/-/DCSupplemental.
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Sensitivity, robustness, and identifiability in stochastic chemical kinetics models
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- Biological Sciences
- Biophysics and Computational Biology
- Physical Sciences
- Applied Mathematics
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