The impact of experimental designs & system sloppiness on the personalisation process: A cardiovascular perspective
Harry Saxton, Daniel J. Taylor, Grace Faulkner, Ian Halliday, Tom Newman, Torsten Schenkel, Paul D. Morris, Richard H. Clayton, Xu Xu
Abstract
To employ a reduced-order cardiovascular model as a digital twin for personalised medicine, it is essential to understand how uncertainties in the model’s input parameters affect its outputs. The aim is to identify a set of input parameters that can serve as clinical biomarkers, providing insight into a patient’s physiological state.
Introduction
The concept of digital twin (DT) originates in the 1960s with NASA creating a virtual representation in the Apollo 13 moon exploration mission. There are now many definitions of DT and one comprehensive definition is “a set of virtual information constructs that mimics the structure, context and behaviour of an individual or unique physical asset.
Methods
Here, we examine the methods used for analysing a lumped parameter 4-chamber cardiovascular circulation model. In section methods, we present the model, explain the computational framework and provide a full parameterisation with their initial conditions.
Results
Sections Results Discrete, Continuos and Mixed detail the average input parameter influence across all outputs for varying experimental design, using the method presented in section Average Parameter Influence.
Discussion
Our study aims to assess the impact of experimental design on the input parameter influence and the system sloppiness. Overall, the results largely agree with previous work: continuous measurements lead to a larger selected subset of input parameters as prime candidates for personalisation in a cardiovascular DT [45, 55, 58].
Conclusions
Our study highlights the importance of the experimental design for the quantification of input parameter influence and the associated sloppiness, for a lumped parameter personalised cardiovascular digital twin. Using a realistic lumped 4-chamber 36-parameter LPM as a test bench, we investigated 48 independent experimental designs.
Citation: Saxton H, Taylor DJ, Faulkner G, Halliday I, Newman T, Schenkel T, et al. (2025) The impact of experimental designs & system sloppiness on the personalisation process: A cardiovascular perspective. PLoS One 20(6): e0326112. https://doi.org/10.1371/journal.pone.0326112
Editor: Pan Li, Institute for Basic Science, REPUBLIC OF KOREA
Received: February 15, 2025; Accepted: May 24, 2025; Published: June 24, 2025
Copyright: © 2025 Saxton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All code used to generate the findings in this study can be found at the Zenodo link https://doi.org/10.5281/zenodo.15332355.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
