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Parameter estimation is a major challenge in computational modeling ofbiological processes. This is especially the case in image-based modeling wherethe inherently quantitative output of the model is measured against image data,which is typically noisy and non-quantitative. In addition, these models canhave a high computational cost, limiting the number of feasible simulations,and therefore rendering most traditional parameter estimation methodsunsuitable. In this paper, we present a pipeline that uses Gaussian processlearning to estimate biological parameters from noisy, non-quantitative imagedata when the model has a high computational cost. This approach is firstsuccessfully tested on a parametric function with the goal of retrieving theoriginal parameters. We then apply it to estimating parameters in a biologicalsetting by fitting artificial in-situ hybridization (ISH) data of thedeveloping murine limb bud. We expect that this method will be of use in avariety of modeling scenarios where quantitative data is missing and the use ofstandard parameter estimation approaches in biological modeling is prohibitedby the computational cost of the model.

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