Dataset Information

A Priori Identifiability Analysis of Cardiovascular Models.

ABSTRACT: Model parameters, estimated from experimentally measured data, can provide insight into biological processes that are not experimentally measurable. Whether this optimized parameter set is a physiologically relevant complement to the experimentally measured data, however, depends on the optimized parameter set being unique, a model property known as a priori global identifiability. However, a priori identifiability analysis is not common practice in the biological world, due to the lack of easy-to-use tools. Here we present a program, Differential Algebra for Identifiability of Systems (DAISY), that facilitates identifiability analysis. We applied DAISY to several cardiovascular models: systemic arterial circulation (Windkessel, T-Tube) and cardiac muscle contraction (complex stiffness, crossbridge cycling-based). All models were globally identifiable except the T-Tube model. In this instance, DAISY was able to provide insight into making the model identifiable. We applied numerical parameter optimization techniques to estimate unknown parameters in a model DAISY found globally identifiable. While all the parameters could be accurately estimated, a sensitivity analysis was first necessary to identify the required experimental data. Global identifiability is a prerequisite for numerical parameter optimization, and in a variety of cardiovascular models, DAISY provided a reliable, fast, and simple platform to provide this identifiability analysis.

SUBMITTER: Kirk JA

PROVIDER: S-EPMC4696755 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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A Priori Identifiability Analysis of Cardiovascular Models.

Kirk Jonathan A JA Saccomani Maria P MP Shroff Sanjeev G SG

Cardiovascular engineering and technology 20131201 4

Model parameters, estimated from experimentally measured data, can provide insight into biological processes that are not experimentally measurable. Whether this optimized parameter set is a physiologically relevant complement to the experimentally measured data, however, depends on the optimized parameter set being unique, a model property known as a priori global identifiability. However, a priori identifiability analysis is not common practice in the biological world, due to the ...[more]

PMID: 26726299

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Project description:BackgroundKinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges.MethodsWe present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph.ResultsOur contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub ( https://github.com/gabora/visid ).ConclusionsOur approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments.

Project description:ObjectiveOur aim is to analyze the identifiability of three commonly used control-oriented models for glucose control in patients with type 1 diabetes (T1D).MethodsStructural and practical identifiability analysis were performed on three published control-oriented models for glucose control in patients with type 1 diabetes (T1D): the subcutaneous oral glucose minimal model (SOGMM), the intensive control insulin-nutrition-glucose (ICING) model, and the minimal model control-oriented (MMC). Structural identifiability was addressed with a combination of the generating series (GS) approach and identifiability tableaus whereas practical identifiability was studied by means of (1) global ranking of parameters via sensitivity analysis together with the Latin hypercube sampling method (LHS) and (2) collinearity analysis among parameters. For practical identifiability and model identification, continuous glucose monitor (CGM), insulin pump, and meal records were selected from a set of patients (n = 5) on continuous subcutaneous insulin infusion (CSII) that underwent a clinical trial in an outpatient setting. The performance of the identified models was analyzed by means of the root mean square (RMS) criterion.ResultsA reliable set of identifiable parameters was found for every studied model after analyzing the possible identifiability issues of the original parameter sets. According to an importance factor ([Formula: see text]), it was shown that insulin sensitivity is not the most influential parameter from the dynamical point of view, that is, is not the parameter impacting the outputs the most of the three models, contrary to what is assumed in the literature. For the test data, the models demonstrated similar performance with most RMS values around 20 mg/dl (min: 15.64 mg/dl, max: 51.32 mg/dl). However, MMC failed to identify the model for patient 4. Also, considering the three models, the MMC model showed the higher parameter variability when reidentified every 6 hours.ConclusionThis study shows that both structural and practical identifiability analysis need to be considered prior to the model identification/individualization in patients with T1D. It was shown that all the studied models are able to represent the CGM data, yet their usefulness in a hypothetical artificial pancreas could be a matter of debate. In spite that the three models do not capture all the dynamics and metabolic effects as a maximal model (ie, our FDA-accepted UVa/Padova simulator), SOGMM and ICING appear to be more appealing than MMC regarding both the performance and parameter variability after reidentification. Although the model predictions of ICING are comparable to the ones of the SOGMM model, the large parameter set makes the model prone to overfitting if all parameters are identified. Moreover, the existence of a high nonlinear function like [Formula: see text] prevents the use of tools from the linear systems theory.

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A Priori Identifiability Analysis of Cardiovascular Models.

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<i>A Priori</i> Identifiability Analysis of Cardiovascular Models.

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