Paper Info
Title
Specifying informative experiment stimulation conditions for resolving dynamical uncertainty in biological systems.
Abstract
A computationally efficient model-based design of experiments (MBDOE) strategy is developed to plan an optimal experiment by specifying the experimental stimulation magnitudes and measurement points. The strategy is extended from previous work which optimized the experimental design over a space of measurable species and time points. We include system inputs (stimulation conditions) in the experiment design search to investigate if the addition of perturbations enhances the ability of the MBDOE method to resolve uncertainties in system dynamics. The MBDOE problem is made computationally tractable by using a sparse-grid approximation of the model output dynamics, pre-specifying the time points at which the input or experimental perturbations can be applied, and creating scenario trees to explore the endogenous uncertainty. Consecutive scenario trees are used to determine the best input magnitudes and select the optimal associated measurement species and time points. We demonstrate the effectiveness of this strategy on a T-Cell Receptor (TCR) signaling pathway model.
Year
DOI
Venue
2014
10.1109/EMBC.2014.6943588
EMBC
Keywords
DocType
Volume
optimisation,model-based design of experiments,biological systems,cellular biophysics,physiological models,informative experiment stimulation conditions,optimization,computationally efficient mbdoe strategy,t-cell receptor signaling pathway model,design of experiments,sparse-grid approximation,dynamical uncertainty
Conference
2014
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
3
4
Name
Order
Citations
PageRank
Thembi Mdluli100.34
Michael Pargett230.76
Gregery T Buzzard310.69
Ann E. Rundell4707.78