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Global optimization using Gaussian processes to estimate biological parameters from image data.

Diana Barac1, Michael D Multerer1, Dagmar Iber1

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Summary
This summary is machine-generated.

Estimating biological parameters from noisy image data is challenging due to high computational costs. This study introduces a Gaussian process learning pipeline to overcome these limitations in computational modeling.

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Area of Science:

  • Computational biology
  • Systems biology
  • Bioinformatics

Background:

  • Parameter estimation is crucial for computational modeling of biological processes.
  • Image-based modeling presents challenges due to noisy, non-quantitative image data.
  • High computational costs of models limit traditional parameter estimation methods.

Purpose of the Study:

  • To develop and validate a novel pipeline for biological parameter estimation.
  • To address limitations posed by noisy data and high computational costs in biological modeling.
  • To enable parameter estimation in scenarios lacking quantitative data.

Main Methods:

  • Utilized Gaussian process learning for parameter estimation.
  • Tested the pipeline on a parametric function to retrieve original parameters.
  • Applied the method to estimate parameters using artificial in-situ hybridization (ISH) data from murine limb bud development.

Main Results:

  • Successfully retrieved parameters from a test parametric function.
  • Demonstrated the pipeline's efficacy in a biological context with simulated ISH data.
  • Validated the approach for noisy, non-quantitative image data.

Conclusions:

  • The Gaussian process learning pipeline effectively estimates biological parameters.
  • This method is suitable for computational modeling with high costs and limited quantitative data.
  • The approach offers a valuable tool for various biological modeling scenarios.