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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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CaliPro: A Calibration Protocol That Utilizes Parameter Density Estimation to Explore Parameter Space and Calibrate

Louis R Joslyn1,2, Denise E Kirschner2, Jennifer J Linderman1

  • 1Department of Chemical Engineering, University of Michigan, G045W NCRC B28, 2800 Plymouth Rd, Ann Arbor, MI 48109-2136 USA.

Cellular and Molecular Bioengineering
|March 1, 2021
PubMed
Summary

CaliPro is a new iterative method for calibrating complex biological models. This model-agnostic protocol refines parameter space for accurate quantitative predictions from temporal datasets.

Keywords:
Alternative density subtractionHighest density regionMathematical modelingParameter estimationParameter space

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Area of Science:

  • Computational Biology
  • Mathematical Modeling
  • Systems Biology

Background:

  • Mathematical and computational models are crucial for understanding biological systems.
  • Accurate quantitative predictions require models calibrated to existing biological datasets.
  • Current calibration methods struggle with complex biological models, often focusing on single data aspects or requiring difficult-to-assign Bayesian priors.

Purpose of the Study:

  • To develop a novel calibration protocol suitable for complex biological models.
  • To address limitations of existing calibration approaches, particularly for temporal datasets.
  • To provide a method that can handle diverse biological systems and calibration goals.

Main Methods:

  • Developed CaliPro, an iterative and model-agnostic calibration protocol.
  • Utilizes parameter density estimation to refine parameter space.
  • Incorporates user-defined pass set definition for successful experimental data recapitulation.

Main Results:

  • Demonstrated CaliPro's utility across four diverse examples: predator-prey, infectious disease, and immune response models.
  • Showcased effectiveness for both deterministic, continuous, and stochastic, discrete model structures.
  • Validated CaliPro's adaptability to various calibration objectives.

Conclusions:

  • Introduced CaliPro, a novel method for calibrating complex biological models to experimental data.
  • CaliPro expedites the calibration process.
  • Potential application in analyzing specific model behaviors within already calibrated parameter spaces.