Royal College of Surgeons in Ireland
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Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation

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journal contribution
posted on 2023-01-30, 17:16 authored by Adrien Coulier, Prashant Singh, Marc SturrockMarc Sturrock, Andreas Hellander

Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order to get accurate and representative results. The choice is important, because models of successively higher fidelity come at a rapidly increasing computational cost. In some situations, the level of detail is clearly motivated by the question under study. In many situations however, many model options could qualitatively agree with available data, depending on the amount of data and the nature of the observations. Here, an important distinction is whether we are interested in inferring the true (but unknown) physical parameters of the model or if it is sufficient to be able to capture and explain available data. The situation becomes complicated from a computational perspective because inference needs to be approximate. Most often it is based on likelihood-free Approximate Bayesian Computation (ABC) and here determining which summary statistics to use, as well as how much data is needed to reach the desired level of accuracy, are difficult tasks. Ultimately, all of these aspects-the model fidelity, the available data, and the numerical choices for inference-interplay in a complex manner. In this paper we develop a computational pipeline designed to systematically evaluate inference accuracy for a wide range of true known parameters. We then use it to explore inference settings for negative feedback gene regulation. In particular, we compare a detailed spatial stochastic model, a coarse-grained compartment-based multiscale model, and the standard well-mixed model, across several data-scenarios and for multiple numerical options for parameter inference. Practically speaking, this pipeline can be used as a preliminary step to guide modelers prior to gathering experimental data. By training Gaussian processes to approximate the distance function values, we are able to substantially reduce the computational cost of running the pipeline. 


Swedish research council (2015-03964)

eSSENCE strategic collaboration of eScience

NIH NIH/2R01EB014877-04A1


Associated research data files

All code and generated data underlying the study is available at Data used for some experiments are taken from Hofmann H, Kafadar K, Wickham H. (2011), and is publicly available as a.json file at


The original article is available at

Published Citation

Coulier A, Singh P, Sturrock M, Hellander A. Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation. PLoS Comput Biol. 2022;18(12):e1010683.

Publication Date

15 December 2022

PubMed ID



  • Physiology and Medical Physics


Public Library of Science


  • Published Version (Version of Record)