Global optimization using Gaussian Processes to estimate biological parameters from image data

TL;DR

This paper introduces a Gaussian process-based pipeline for estimating biological parameters from noisy, non-quantitative image data in high-cost models, demonstrated on both synthetic and biological data.

Contribution

The paper presents a novel Gaussian process approach tailored for high-cost biological models using noisy image data, filling a gap in existing parameter estimation methods.

Findings

Successfully retrieved original parameters from synthetic data.

Accurately estimated biological parameters from murine limb bud data.

Method reduces computational burden in parameter estimation.

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.