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

Authors
  • Barac, Diana1
  • Multerer, Michael D2
  • Iber, Dagmar3
  • 1 Department for Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel 4058, Switzerland; Swiss Institute of Bioinformatics (SIB), Mattenstrasse 26, Basel 4058, Switzerland. Electronic address: [email protected] , (Switzerland)
  • 2 Department for Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel 4058, Switzerland; Swiss Institute of Bioinformatics (SIB), Mattenstrasse 26, Basel 4058, Switzerland. Electronic address: [email protected] , (Switzerland)
  • 3 Department for Biosystems Science and Engineering, ETH Zurich, Mattenstrasse 26, Basel 4058, Switzerland; Swiss Institute of Bioinformatics (SIB), Mattenstrasse 26, Basel 4058, Switzerland. Electronic address: [email protected] , (Switzerland)
Type
Published Article
Journal
Journal of Theoretical Biology
Publisher
Elsevier
Publication Date
Nov 21, 2019
Volume
481
Pages
233–248
Identifiers
DOI: 10.1016/j.jtbi.2018.12.002
PMID: 30529487
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Parameter estimation is a major challenge in computational modeling of biological processes. This is especially the case in image-based modeling where the inherently quantitative output of the model is measured against image data, which is typically noisy and non-quantitative. In addition, these models can have a high computational cost, limiting the number of feasible simulations, and therefore rendering most traditional parameter estimation methods unsuitable. In this paper, we present a pipeline that uses Gaussian process learning to estimate biological parameters from noisy, non-quantitative image data when the model has a high computational cost. This approach is first successfully tested on a parametric function with the goal of retrieving the original parameters. We then apply it to estimating parameters in a biological setting by fitting artificial in-situ hybridization (ISH) data of the developing murine limb bud. We expect that this method will be of use in a variety of modeling scenarios where quantitative data is missing and the use of standard parameter estimation approaches in biological modeling is prohibited by the computational cost of the model. Copyright © 2018 Elsevier Ltd. All rights reserved.

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