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Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
Regulation of Heart Rates01:31

Regulation of Heart Rates

The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
The SNS increases heart rate through the release of norepinephrine and epinephrine, which act on beta-1 adrenergic receptors in the heart. This action increases the rate of depolarization in the sinoatrial (SA) node, the heart's...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

Cardiac Output I:Effect of Heart Rate on Cardiac Output

Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
Effect of Heart Rate on Cardiac Output
Cardiac output adapts to metabolic demands during stress, physical activity, or illness. The autonomic nervous system regulates heart rate via the sinoatrial node. The parasympathetic nervous system decreases heart rate...

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Related Experiment Video

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A practical approach to parameter estimation applied to model predicting heart rate regulation.

Mette S Olufsen1, Johnny T Ottesen

  • 1Department of Science, Systems, and Models, Roskilde University, Universitetsvej 1, 4000, Roskilde, Denmark. msolufse@ncsu.edu

Journal of Mathematical Biology
|May 17, 2012
PubMed
Summary

Identifying patient-specific parameters in biological models is crucial. This study compares three methods for selecting estimable parameter subsets, finding the structured correlation method most effective, though computationally intensive.

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Area of Science:

  • Computational Biology
  • Mathematical Modeling
  • Physiological Systems Analysis

Background:

  • Mathematical models are vital for predicting biological system dynamics.
  • Patient-specific models enhance predictive accuracy.
  • Parameter estimation is key for personalized biological models.

Purpose of the Study:

  • To compare three distinct methods for identifying estimable parameter subsets in complex biological models.
  • To apply these methods for patient-specific parameter estimation in baroreceptor feedback regulation of heart rate.
  • To evaluate the trade-offs between computational cost and subset quality.

Main Methods:

  • Structured analysis of the correlation matrix.
  • Singular value decomposition (SVD) followed by QR factorization.
  • Identification of the subspace closest to the one spanned by eigenvectors of the model Hessian.

Main Results:

  • All three methods successfully identified parameter subsets for estimation.
  • The structured correlation method yielded the "best" subset but was most computationally intensive.
  • SVD/QR and eigenvector methods were faster but resulted in subsets with correlated parameters.

Conclusions:

  • Parameter subset identification is feasible using multiple computational approaches.
  • A combination of these methods may offer an efficient strategy to balance computational load and subset quality.
  • Optimizing parameter subset selection is critical for accurate patient-specific biological modeling.