Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2009-11-13
pubmed:abstractText
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.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-12432404, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-15499023, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-15796538, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-15908919, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-16204838, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-16239477, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-16990515, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-17091579, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-17591178, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-17922568, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-18158244, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-18282085, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-18366722, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-18471983, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-18676830, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-18681746, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-18793133, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-19156131, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-19215298, http://linkedlifedata.com/resource/pubmed/commentcorrection/19911077-19640164
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1553-7358
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
e1000558
pubmed:dateRevised
2011-2-14
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Optimal experimental design for parameter estimation of a cell signaling model.
pubmed:affiliation
Department of Chemical and Systems Biology, Stanford University, Stanford, California, USA. sbandara@stanford.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural