Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model Export

@article{citeulike:6080017, abstract = {Differential equation models that describe the dynamic changes of biochemical signaling states are important tools to understand cellular behavior. An essential task in building such representations is to infer the affinities, rate constants, and other parameters of a model from actual measurement data. However, intuitive measurement protocols often fail to generate data that restrict the range of possible parameter values. Here we utilized a numerical method to iteratively design optimal live-cell fluorescence microscopy experiments in order to reveal pharmacological and kinetic parameters of a phosphatidylinositol 3,4,5-trisphosphate (PIP 3 ) second messenger signaling process that is deregulated in many tumors. The experimental approach included the activation of endogenous phosphoinositide 3-kinase (PI3K) by chemically induced recruitment of a regulatory peptide, reversible inhibition of PI3K using a kinase inhibitor, and monitoring of the PI3K-mediated production of PIP 3 lipids using the pleckstrin homology (PH) domain of Akt. We found that an intuitively planned and established experimental protocol did not yield data from which relevant parameters could be inferred. Starting from a set of poorly defined model parameters derived from the intuitively planned experiment, we calculated concentration-time profiles for both the inducing and the inhibitory compound that would minimize the predicted uncertainty of parameter estimates. Two cycles of optimization and experimentation were sufficient to narrowly confine the model parameters, with the mean variance of estimates dropping more than sixty-fold. Thus, optimal experimental design proved to be a powerful strategy to minimize the number of experiments needed to infer biological parameters from a cell signaling assay.}, author = {Bandara,, Samuel and Schl\"{o}der,, Johannes .. and Eils,, Roland and Bock,, Hans e. o. r. g. and Meyer,, Tobias}, citeulike-article-id = {6080017}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1000558}, day = {6}, doi = {10.1371/journal.pcbi.1000558}, issn = {1553-7358}, journal = {PLoS Computational Biology}, keywords = {differential\_equation\_models, estimation, model\_parameters, parameter\_estimation}, month = {November}, number = {11}, pages = {e1000558+}, posted-at = {2009-11-06 06:44:09}, priority = {5}, publisher = {Public Library of Science}, title = {Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model}, url = {http://dx.doi.org/10.1371/journal.pcbi.1000558}, volume = {5}, year = {2009} }

TY - JOUR ID - citeulike:6080017 L3 - citeulike-article-id:6080017 N2 - Differential equation models that describe the dynamic changes of biochemical signaling states are important tools to understand cellular behavior. An essential task in building such representations is to infer the affinities, rate constants, and other parameters of a model from actual measurement data. However, intuitive measurement protocols often fail to generate data that restrict the range of possible parameter values. Here we utilized a numerical method to iteratively design optimal live-cell fluorescence microscopy experiments in order to reveal pharmacological and kinetic parameters of a phosphatidylinositol 3,4,5-trisphosphate (PIP 3 ) second messenger signaling process that is deregulated in many tumors. The experimental approach included the activation of endogenous phosphoinositide 3-kinase (PI3K) by chemically induced recruitment of a regulatory peptide, reversible inhibition of PI3K using a kinase inhibitor, and monitoring of the PI3K-mediated production of PIP 3 lipids using the pleckstrin homology (PH) domain of Akt. We found that an intuitively planned and established experimental protocol did not yield data from which relevant parameters could be inferred. Starting from a set of poorly defined model parameters derived from the intuitively planned experiment, we calculated concentration-time profiles for both the inducing and the inhibitory compound that would minimize the predicted uncertainty of parameter estimates. Two cycles of optimization and experimentation were sufficient to narrowly confine the model parameters, with the mean variance of estimates dropping more than sixty-fold. Thus, optimal experimental design proved to be a powerful strategy to minimize the number of experiments needed to infer biological parameters from a cell signaling assay. IS - 11 JF - PLoS Computational Biology SN - 1553-7358 TI - Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model PB - Public Library of Science VL - 5 SP - e1000558 KW - differential_equation_models KW - estimation KW - model_parameters KW - parameter_estimation AU - Bandara,, Samuel AU - Schlöder,, Johannes AU - Eils,, Roland AU - Bock,, Hans AU - Meyer,, Tobias PY - 2009/11/06/ UR - http://dx.doi.org/10.1371/journal.pcbi.1000558 ER -