Mathematical models of the circulation continue to be an essential tool to study how the cardiovascular (CV) system maintains homeostasis. The utility of these models is ultimately limited by how much we can trust the accuracy of their predictions . The predictive capability of a model can be measured by uncertainty quantification (UQ). A challenge in implementing UQ procedures is that many published methods require that the model be identifiable . An identifiable model is one with a one-to-one mapping  from the parameter space to the model output  . In this paper we use a novel and reproducible methodology to calibrate a lumped-parameter CV model to left ventricular pressure and volume time series data from rats . Key steps in our methodology include using (1) literature and available data to define a set of nominal parameter values specific to each rat ; (2) sensitivity analysis and subset selection to determine a set of identifiable parameters; (3) optimization to find a point estimate for identifiable parameters; and (4) both frequentist and Bayesian UQ methods to assess the predictive capability of the model  .
Mathematical models are essential tools to study how the cardiovascular system maintains homeostasis. The utility of such models is limited by the accuracy of their predictions , which can be determined by uncertainty quantification (UQ). A challenge associated with the use of UQ is that many published methods assume that the underlying model is identifiable (e.g. that a one-to-one mapping exists from the parameter space to the model output ) . In this study we present a novel methodology that is used here to calibrate a lumped-parameter model to left ventricular pressure and volume time series data sets . Key steps include using (1) literature and available data to determine nominal parameter values ; (2) sensitivity analysis and subset selection to determine a set of identifiable parameters; (3) optimization to find a point estimate for identifiable parameters; and (4) frequentist and Bayesian UQ calculations to assess the predictive capability of the model . Our results show that it is possible to determine 5 identifiable model parameters that can be estimated to our experimental data from three rats, and that computed UQ intervals capture the measurement and model error .
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of the circulation continue to be an essential tool
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(CV)
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these models is ultimately limited by how much we can trust
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. The predictive capability of a model can be measured
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in implementing UQ procedures
associated with the use of UQ
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require that the model be identifiable . An identifiable model is one with
assume that the underlying model is identifiable (e.g. that
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exists
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)
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paper we use a novel and reproducible methodology
study we present a novel methodology that is used here
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CV
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in our methodology
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specific to each rat
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both
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. Our results show that it is possible to determine 5 identifiable model parameters that can be estimated to our experimental data from three rats, and that computed UQ intervals capture the measurement and model error