Biomedical model fitting and error analysis.
Biomedical model fitting and error analysis.
复制标题
生物医学模型拟合和误差分析。
DOI：
10.1126/scisignal.2001983
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发表时间：
2011-09-20
影响因子：
7.3
通讯作者：
Hershberg U
中科院分区：
文献类型：
--
作者：
Costa KD;Kleinstein SH;Hershberg U
该教学资源向学生介绍曲线拟合和误差分析；这是关于开发生物医学系统数学模型的两场讲座中的第二场。第一个重点是从实验文献中识别、提取和转换所需的常数，例如动力学速率常数。为了了解如何根据实验数据确定这些常数，本讲座介绍了将数学模型拟合到一系列测量值的原理和实践。我们强调使用非线性模型来拟合非线性数据，避免与可能扭曲和歪曲数据的线性化方案相关的问题。为了帮助确保正确解释逆向建模估计的模型参数，我们描述了严格的六步过程：（i）选择适当的数学模型； (ii) 定义量化模型和数据之间误差的“品质因数”函数； (iii) 调整模型参数以获得数据的“最佳拟合”； (iv) 检查数据的“拟合优度”； (v) 确定是否可能有更好的拟合； (vi) 评估最佳拟合参数值的准确性。计算方法的实现基于 MATLAB，并提供了可以针对特定应用进行修改的示例程序。该问题集允许学生使用这些程序，在使用 BrdU 标记实验数据确定 B 淋巴细胞的细胞增殖和死亡速率的背景下，培养逆向建模过程的实践经验。
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.
影响因子：
4.6
作者：
LUTCHEN, KR;COSTA, KD
通讯作者：
COSTA, KD
DOI：
10.4049/jimmunol.0902452
发表时间：
2009-12-01
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Anderson SM;Khalil A;Uduman M;Hershberg U;Louzoun Y;Haberman AM;Kleinstein SH;Shlomchik MJ
通讯作者：
Shlomchik MJ
影响因子：
15
作者：
Lineweaver, H;Burk, D
通讯作者：
Burk, D