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The Simulation, Fitting, and Testing of a Stochastic Cellular Proliferation Model
David G. Hoel and Toby J. Mitchell
Vol. 27, No. 1 (Mar., 1971), pp. 191-199
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2528937
Page Count: 9
You can always find the topics here!Topics: Simulations, Modeling, Economic growth models, Population growth, Parametric models, Stochastic models, Data models, Mathematical growth, Mathematical functions, Cell growth
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A stochastic model for the growth of a cell population is proposed, and is studied by means of simulations on a digital computer. In order to fit this model to experimental data, repeated computer simulations are performed and the `distance' between the data and the simulated trials is studied. By viewing the expectation of this `distance' as a response surface over the parameter space of the model, standard response surface methods may be used to optimize the fit. This technique is used to fit several competing models to some data of Kubitscheck  on the growth of colonies of E. coli cells. Monte Carlo procedures for testing the `goodness of fit' of these models are proposed and carried out. Although these methods are unrefined, they permit the experimenter to consider his data in the light of stochastic models for which mathematical results are not available.
Biometrics © 1971 International Biometric Society