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Open AccessFeature PaperArticle

Optimal Experimental Design for Parameter Estimation of an IL-6 Signaling Model

by Andrew Sinkoe 1,2 and Juergen Hahn 1,2,3,*
1
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
2
Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
3
Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
*
Author to whom correspondence should be addressed.
Processes 2017, 5(3), 49; https://doi.org/10.3390/pr5030049
Received: 16 June 2017 / Revised: 25 July 2017 / Accepted: 22 August 2017 / Published: 1 September 2017
(This article belongs to the Special Issue Biological Networks)
IL-6 signaling plays an important role in inflammatory processes in the body. While a number of models for IL-6 signaling are available, the parameters associated with these models vary from case to case as they are non-trivial to determine. In this study, optimal experimental design is utilized to reduce the parameter uncertainty of an IL-6 signaling model consisting of ordinary differential equations, thereby increasing the accuracy of the estimated parameter values and, potentially, the model itself. The D-optimality criterion, operating on the Fisher information matrix and, separately, on a sensitivity matrix computed from the Morris method, was used as the objective function for the optimal experimental design problem. Optimal input functions for model parameter estimation were identified by solving the optimal experimental design problem, and the resulting input functions were shown to significantly decrease parameter uncertainty in simulated experiments. Interestingly, the determined optimal input functions took on the shape of PRBS signals even though there were no restrictions on their nature. Future work should corroborate these findings by applying the determined optimal experimental design on a real experiment. View Full-Text
Keywords: optimal experimental design; D-optimality criterion; Fisher information matrix; sensitivity analysis; IL-6 signaling; parameter estimation; piecewise constant functions optimal experimental design; D-optimality criterion; Fisher information matrix; sensitivity analysis; IL-6 signaling; parameter estimation; piecewise constant functions
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Sinkoe, A.; Hahn, J. Optimal Experimental Design for Parameter Estimation of an IL-6 Signaling Model. Processes 2017, 5, 49.

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