Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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)...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Clearance Models: Physiological Models01:09

Clearance Models: Physiological Models

Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
The organ's clearance rate depends on the blood flow to the organ and the extraction ratio (E). The extraction ratio describes the organ's proficiency in drug...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Transcranial direct current stimulation targeting the left dorsolateral prefrontal cortex increases gamma power and standing balance in older adults.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Intensity matters: vigorous activity is associated with lower pressure-independent arterial stiffness in the UK Biobank.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same author

Characterization of EEG power spectrum correlates of standing and walking in older adults: A scoping review.

Experimental gerontology·2026
Same author

Arterial stiffness moderates the link between NfL and cognition: The IGNITE study.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Berries and Steps: a protocol of a randomized, placebo-controlled pilot study testing freeze-dried blueberry powder in sedentary older adults with mild depressive symptoms.

Nutrition journal·2025
Same author

Rationale and Design of STAMINA: Senolytics To Alleviate Mobility Issues and Neurological Impairments in Aging, A Geroscience Feasibility Study.

Translational medicine of aging·2025

Related Experiment Video

Updated: Jun 28, 2026

Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

Dynamic models for the study of frailty.

Lewis A Lipsitz1

  • 1Hebrew SeniorLife Institute for Aging Research, Beth Israel Deaconess Medical Center Gerontology Division, Harvard Medical School, 1200 Centre Street, Boston, MA 02131, United States. Lipsitz@hrca.harvard.edu

Mechanisms of Ageing and Development
|October 22, 2008
PubMed
Summary

Mathematical models can quantify frailty by analyzing physiological system dynamics. A novel signaling network model reveals how degraded pathways cause loss of complexity, characterizing frailty.

More Related Videos

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

Identifying Frailty Using Point-of-Care Ultrasonography: Image Acquisition and Assessment
04:00

Identifying Frailty Using Point-of-Care Ultrasonography: Image Acquisition and Assessment

Published on: July 26, 2024

Related Experiment Videos

Last Updated: Jun 28, 2026

Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

Identifying Frailty Using Point-of-Care Ultrasonography: Image Acquisition and Assessment
04:00

Identifying Frailty Using Point-of-Care Ultrasonography: Image Acquisition and Assessment

Published on: July 26, 2024

Area of Science:

  • Physiology
  • Complexity Science
  • Mathematical Modeling

Background:

  • Frailty arises from the degradation of interacting physiological systems essential for adaptation.
  • Current mathematical models, like stimulus-response, offer limited scope for characterizing frailty's complex dynamics.
  • A need exists for models capturing the diverse temporal behaviors of physiological systems in frailty.

Discussion:

  • A novel signaling network model, using a lattice of nodes and connections, replicates the fractal-like dynamics of healthy physiological processes.
  • This model demonstrates how degradation in signaling pathways leads to a loss of complexity, a hallmark of frailty.
  • The model provides a framework for understanding the dynamic alterations underlying frailty.

Key Insights:

  • Frailty is characterized by a loss of complex dynamics in physiological systems.
  • Degradation of signaling pathways is a key mechanism contributing to frailty.
  • A lattice-based signaling network model can effectively simulate physiological complexity and its loss.

Outlook:

  • This model can be extended to explore interventions targeting the restoration of physiological complexity.
  • Further research can investigate how different network structures and degradation patterns influence frailty.
  • The model offers a quantitative approach to studying aging and resilience.