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Title: Data-Driven Model Validation Across Dimensions

Abstract

Data-driven model validation across dimensions in mathematical and computational biology assumptions are often made (e.g., symmetry) to reduce the problem from three spatial dimensions (3D) to two (2D). However, some experimental datasets, such as cell counts obtained via flow cytometry, represent the entire 3D biological object. For purpose of model calibration and validation, it is sometimes necessary to compare these biological datasets with model outputs. We propose a methodology for scaling 2D model outputs to compare with 3D experimental datasets, and we discuss the application of this methodology to two examples: agent-based models of granuloma formation and skeletal muscle tissue. The accuracy of the method is evaluated in artificially generated scenarios.

Authors:
 [1];  [2];  [3];  [4];  [3]; ORCiD logo [1]
  1. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Microbiology and Immunology
  2. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Microbiology and Immunology; Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Chemical Engineering
  3. Univ. of Virginia, Charlottesville, VA (United States). Dept. of Biomedical Engineering
  4. Univ. of Michigan, Ann Arbor, MI (United States). Dept. of Chemical Engineering
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory-National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1526985
DOE Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article
Journal Name:
Bulletin of Mathematical Biology
Additional Journal Information:
Journal Volume: 81; Journal Issue: 6; Journal ID: ISSN 0092-8240
Country of Publication:
United States
Language:
English

Citation Formats

Renardy, Marissa, Wessler, Timothy, Blemker, Silvia, Linderman, Jennifer, Peirce, Shayn, and Kirschner, Denise. Data-Driven Model Validation Across Dimensions. United States: N. p., 2019. Web. doi:10.1007/s11538-019-00590-4.
Renardy, Marissa, Wessler, Timothy, Blemker, Silvia, Linderman, Jennifer, Peirce, Shayn, & Kirschner, Denise. Data-Driven Model Validation Across Dimensions. United States. doi:10.1007/s11538-019-00590-4.
Renardy, Marissa, Wessler, Timothy, Blemker, Silvia, Linderman, Jennifer, Peirce, Shayn, and Kirschner, Denise. Mon . "Data-Driven Model Validation Across Dimensions". United States. doi:10.1007/s11538-019-00590-4.
@article{osti_1526985,
title = {Data-Driven Model Validation Across Dimensions},
author = {Renardy, Marissa and Wessler, Timothy and Blemker, Silvia and Linderman, Jennifer and Peirce, Shayn and Kirschner, Denise},
abstractNote = {Data-driven model validation across dimensions in mathematical and computational biology assumptions are often made (e.g., symmetry) to reduce the problem from three spatial dimensions (3D) to two (2D). However, some experimental datasets, such as cell counts obtained via flow cytometry, represent the entire 3D biological object. For purpose of model calibration and validation, it is sometimes necessary to compare these biological datasets with model outputs. We propose a methodology for scaling 2D model outputs to compare with 3D experimental datasets, and we discuss the application of this methodology to two examples: agent-based models of granuloma formation and skeletal muscle tissue. The accuracy of the method is evaluated in artificially generated scenarios.},
doi = {10.1007/s11538-019-00590-4},
journal = {Bulletin of Mathematical Biology},
issn = {0092-8240},
number = 6,
volume = 81,
place = {United States},
year = {2019},
month = {3}
}

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