David A. Hormuth, II

Research Scientist | Biomedical Engineering + Imaging Science > > Computational Oncology

A generalized framework for model

Journal article

Inês G. Gonçalves, D. Hormuth, Sandhya Prabhakaran, M. Caleb, Phillips

Semantic Scholar


APA   Click to copy
Gonçalves, I. G., Hormuth, D., Prabhakaran, S., Caleb, M., & Phillips. (2022). A generalized framework for model.

Chicago/Turabian   Click to copy
Gonçalves, Inês G., D. Hormuth, Sandhya Prabhakaran, M. Caleb, and Phillips. “A Generalized Framework for Model” (2022).

MLA   Click to copy
Gonçalves, Inês G., et al. A Generalized Framework for Model. 2022.

BibTeX   Click to copy

  title = {A generalized framework for model},
  year = {2022},
  author = {Gonçalves, Inês G. and Hormuth, D. and Prabhakaran, Sandhya and Caleb, M. and Phillips}


12 In silicomodels of biological systems are usually very complex and rely on a large number of param13 eters describing physical and biological properties that require validation. As such, exploration of 14 parameter space is an essential component of computational model development to fully charac15 terize and validate simulation results. Experimental data may also be used to constrain parameter 16 space (or enable model calibration) to enhance the biological relevance tomodel parameters. One 17 widely used computational platform in themathematical biology community is PhysiCellwhich pro18 vides a standardized approach to agent-based models of biological phenomena at different time 19 and spatial scales. One limitation of PhysiCell, however, is that there has not been a generalized ap20 proach for parameter space exploration and calibration that can be run without high-performance 21 computing access. Taking this into account, we present PhysiCOOL, an open-source Python library 22 tailored to create standardized calibration and optimization routines of PhysiCellmodels. 23 Graphical abstract 24 Gonçalves et al. | bioRχ iv | November 17, 2022 | 1–8


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