Data-Driven Model Validation Across Dimensions
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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.
KeywordsScaling Agent–based models Model validation Model calibration Parameter estimation using data
We thank Josh Mattila for the stained granuloma image, Joanne Flynn for the monkey lung image, and Caitlin Hult for generating the 3D GranSim image (all shown in Fig. 1). This research was supported by the following NIH Grants awarded to JL and DK: R01 AI123093 and U01HL131072. SB and SP are supported by NIH Grant U01AR069393. This research also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 and the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant Number MCB140228.
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