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Accepted (AM)Open Access (License Unspecified), Open
Journal article
Biomedical Model Fitting and Error Analysis
Science signaling, v 4(192), pp tr9-tr9
27 Sep 2011
PMID: 21954296
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- Biomedical Model Fitting and Error Analysis
- Creators
- Kevin D. Costa - Icahn School of Medicine at Mount SinaiSteven H. Kleinstein - Yale UniversityUri Hershberg - Drexel University
- Publication Details
- Science signaling, v 4(192), pp tr9-tr9
- Publisher
- Amer Assoc Advancement Science
- Number of pages
- 4
- Grant note
- National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH); United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI) P50GM071558 / NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS) P50 GM071558 / Systems Biology Center HHSN268201000045C; HH-SN2662000500021C; HHSN272201000054C / National Institute of Allergy and Infectious Diseases (NIAID), NIH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000295257400004
- Scopus ID
- 2-s2.0-80053266761
- Other Identifier
- 991019167549704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biochemistry & Molecular Biology
- Cell Biology