UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering
Akil Narayan1, Zexin Liu1, Jake A Bergquist2
1Scientific Computing and Imaging Institute, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States; Department of Mathematics, University of Utah, 72 Central Campus Dr, Salt Lake City, UT, 84112, United States.
Computers in Biology and Medicine
|December 15, 2022
Summary
A new software tool, UncertainSCI, quantifies how parameter variability affects biomedical simulations. This open-source Python tool helps analyze uncertainty in models, improving computational biology research.
Area of Science:
- Computational Biology
- Biomedical Engineering
- Scientific Computing
Background:
- Biomedical simulations rely on parameters with assumed values.
- Quantifying the impact of parameter variability is crucial but lacks general tools.
- Parametric uncertainty analysis is a growing area of interest in computational medicine.
Purpose of the Study:
- To develop a general-purpose software tool for quantifying uncertainty in biomedical simulations.
- To provide a solution for analyzing the effects of parametric variability on model outputs.
- To facilitate the study of uncertainty due to parameter variations in computational models.
Main Methods:
- Developed UncertainSCI, an open-source Python software suite.
- Utilized advanced parameter sampling and polynomial chaos (PC) emulation.
- Modeled parameters as random variables to compute output statistics and sensitivities.
- Employed near-optimal sampling and PC construction techniques using weighted Fekete points.
Main Results:
- Demonstrated UncertainSCI's utility in analyzing bioelectric potentials in the heart.
- Showcased application in modeling electric stimulation in the brain.
- Successfully estimated variability, statistics, and sensitivities for multiple model parameters.
Conclusions:
- UncertainSCI offers a powerful, lightweight solution for probing parametric variability and uncertainty.
- The non-intrusive pipeline enables integration with existing biomedical simulation software.
- Facilitates accurate ascertainment of parametric uncertainty across diverse applications.
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