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. 2011 Sep 20;4(192):tr9.
doi: 10.1126/scisignal.2001983.

Biomedical Model Fitting and Error Analysis

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Free PMC article

Biomedical Model Fitting and Error Analysis

Kevin D Costa et al. Sci Signal. .
Free PMC article

Abstract

This Teaching Resource introduces students to curve fitting and error analysis; it is the second of two lectures on developing mathematical models of biomedical systems. The first focused on identifying, extracting, and converting required constants--such as kinetic rate constants--from experimental literature. To understand how such constants are determined from experimental data, this lecture introduces the principles and practice of fitting a mathematical model to a series of measurements. We emphasize using nonlinear models for fitting nonlinear data, avoiding problems associated with linearization schemes that can distort and misrepresent the data. To help ensure proper interpretation of model parameters estimated by inverse modeling, we describe a rigorous six-step process: (i) selecting an appropriate mathematical model; (ii) defining a "figure-of-merit" function that quantifies the error between the model and data; (iii) adjusting model parameters to get a "best fit" to the data; (iv) examining the "goodness of fit" to the data; (v) determining whether a much better fit is possible; and (vi) evaluating the accuracy of the best-fit parameter values. Implementation of the computational methods is based on MATLAB, with example programs provided that can be modified for particular applications. The problem set allows students to use these programs to develop practical experience with the inverse-modeling process in the context of determining the rates of cell proliferation and death for B lymphocytes using data from BrdU-labeling experiments.

Figures

Fig. 1
Fig. 1
Schematic model of BrdU cell labeling experiment. The model has two cell populations: unlabeled (U) and BrdU-labeled (L). The proliferation rate (p), and the death rate (d) are the same for unlabeled as for labeled cells. There is an influx of unlabeled cells (s) originating from lymphatic sources. The effect of neglecting this cell source can be examined by enforcing s = 0 in the model (see exercise 6 for details).

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