ArticleJournal of mathematical biology2013
A practical approach to parameter estimation applied to model predicting heart rate regulation.
Article in Journal of mathematical biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
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- Guidelines for mechanistic modeling and analysis in cardiovascular research.American journal of physiology. Heart and circulatory physiology · 2024Review
- Characterization of differences in immune responses during bolus and continuous infusion endotoxin challenges using mathematical modelling.Experimental physiology · 2024Article
- Simulating tumor volume dynamics in response to radiotherapy: Implications of model selection.Journal of theoretical biology · 2024Article
- Fitting parameters and therapies of ODE tumor models with senescence and immune system.Journal of mathematical biology · 2023Article
- iCVS-Inferring Cardio-Vascular hidden States from physiological signals available at the bedside.PLoS computational biology · 2023Article
- Mathematical model for glutathione dynamics in the retina.Scientific reports · 2023Article
- Effective dose window for containing tumor burden under tolerable level.NPJ systems biology and applications · 2023Article
- A survey of open questions in adaptive therapy: Bridging mathematics and clinical translation.eLife · 2023Article
- Parameter inference in a computational model of haemodynamics in pulmonary hypertension.Journal of the Royal Society, Interface · 2023Article
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- Global sensitivity analysis informed model reduction and selection applied to a Valsalva maneuver model.Journal of theoretical biology · 2021Article
- The roles of T cell competition and stochastic extinction events in chimeric antigen receptor T cell therapy.Proceedings. Biological sciences · 2021Article
- Deep phenotyping of cardiac function in heart transplant patients using cardiovascular system models.The Journal of physiology · 2020Article
- Toward a Taxonomy for Analyzing the Heart Rate as a Physiological Indicator of Posttraumatic Stress Disorder: Systematic Review and Development of a Framework.JMIR mental health · 2020Review
- Investigating Macrophages Plasticity Following Tumour-Immune Interactions During Oncolytic Therapies.Acta biotheoretica · 2019Article
- A model-based analysis of autonomic nervous function in response to the Valsalva maneuver.Journal of applied physiology (Bethesda, Md. : 1985) · 2019Article
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2 authors.
Funding
Abstract
Mathematical models have long been used for prediction of dynamics in biological systems. Recently, several efforts have been made to render these models patient specific. One way to do so is to employ techniques to estimate parameters that enable model based prediction of observed quantities. Knowledge of variation in parameters within and between groups of subjects have potential to provide insight into biological function. Often it is not possible to estimate all parameters in a given model, in particular if the model is complex and the data is sparse. However, it may be possible to estimate a subset of model parameters reducing the complexity of the problem. In this study, we compare three methods that allow identification of parameter subsets that can be estimated given a model and a set of data. These methods will be used to estimate patient specific parameters in a model predicting baroreceptor feedback regulation of heart rate during head-up tilt. The three methods include: structured analysis of the correlation matrix, analysis via singular value decomposition followed by QR factorization, and identification of the subspace closest to the one spanned by eigenvectors of the model Hessian. Results showed that all three methods facilitate identification of a parameter subset. The "best" subset was obtained using the structured correlation method, though this method was also the most computationally intensive. Subsets obtained using the other two methods were easier to compute, but analysis revealed that the final subsets contained correlated parameters. In conclusion, to avoid lengthy computations, these three methods may be combined for efficient identification of parameter subsets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.