Dataset Information

Impact of different fixed flow sampling protocols on flow-independent exhaled nitric oxide parameter estimates using the Bayesian dynamic two-compartment model.

ABSTRACT: Exhaled nitric oxide (FeNO) is an established respiratory biomarker with clinical applications in the diagnosis and management of asthma. Because FeNO depends strongly on the flow (exhalation) rate, early protocols specified that measurements should be taken when subjects exhaled at a fixed rate of 50 ml/s. Subsequently, multiple flow (or "extended") protocols were introduced which measure FeNO across a range of fixed flow rates, allowing estimation of parameters including Caw NO and CA NO which partition the physiological sources of NO into proximal airway wall tissue and distal alveolar regions (respectively). A recently developed dynamic model of FeNO uses flow-concentration data from the entire exhalation maneuver rather than plateau means, permitting estimation of Caw NO and CA NO from a wide variety of protocols. In this paper, we use a simulation study to compare Caw NO and CA NO estimation from a variety of fixed flow protocols, including: single maneuvers (30, 50,100, or 300 ml/s) and three established multiple maneuver protocols. We quantify the improved precision with multiple maneuvers and the importance of low flow maneuvers in estimating Caw NO. We conclude by applying the dynamic model to FeNO data from 100 participants of the Southern California Children's Health Study, establishing the feasibility of using the dynamic method to reanalyze archived online FeNO data and extract new information on Caw NO and CA NO in situations where these estimates would have been impossible to obtain using traditional steady-state two compartment model estimation methods.

SUBMITTER: Muchmore P

PROVIDER: S-EPMC6971414 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Impact of different fixed flow sampling protocols on flow-independent exhaled nitric oxide parameter estimates using the Bayesian dynamic two-compartment model.

Muchmore Patrick P Xu Shujing S Marjoram Paul P Rappaport Edward B EB Weng Jingying J Molshatzki Noa N Eckel Sandrah P SP

Physiological reports 20200101 1

Exhaled nitric oxide (FeNO) is an established respiratory biomarker with clinical applications in the diagnosis and management of asthma. Because FeNO depends strongly on the flow (exhalation) rate, early protocols specified that measurements should be taken when subjects exhaled at a fixed rate of 50 ml/s. Subsequently, multiple flow (or "extended") protocols were introduced which measure FeNO across a range of fixed flow rates, allowing estimation of parameters including C<sub>aw</sub> NO and ...[more]

PMID: 31960619

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Project description:MotivationComputational models in biology are frequently underdetermined, due to limits in our capacity to measure biological systems. In particular, mechanistic models often contain parameters whose values are not constrained by a single type of measurement. It may be possible to achieve better model determination by combining the information contained in different types of measurements. Bayesian statistics provides a convenient framework for this, allowing a quantification of the reduction in uncertainty with each additional measurement type. We wished to explore whether such integration is feasible and whether it can allow computational models to be more accurately determined.ResultsWe created an ordinary differential equation model of cell cycle regulation in budding yeast and integrated data from 13 different studies covering different experimental techniques. We found that for some parameters, a single type of measurement, relative time course mRNA expression, is sufficient to constrain them. Other parameters, however, were only constrained when two types of measurements were combined, namely relative time course and absolute transcript concentration. Comparing the estimates to measurements from three additional, independent studies, we found that the degradation and transcription rates indeed matched the model predictions in order of magnitude. The predicted translation rate was incorrect however, thus revealing a deficiency in the model. Since this parameter was not constrained by any of the measurement types separately, it was only possible to falsify the model when integrating multiple types of measurements. In conclusion, this study shows that integrating multiple measurement types can allow models to be more accurately determined.Availability and implementationThe models and files required for running the inference are included in the Supplementary information.Contactl.wessels@nki.nl.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Impact of different fixed flow sampling protocols on flow-independent exhaled nitric oxide parameter estimates using the Bayesian dynamic two-compartment model.

Publications

Impact of different fixed flow sampling protocols on flow-independent exhaled nitric oxide parameter estimates using the Bayesian dynamic two-compartment model.

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