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

Aging01:26

Aging

456
Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
Cellular Clock Theory
The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
456
The Effect of Aging on Tissues01:19

The Effect of Aging on Tissues

3.0K
Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
3.0K
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

87
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
87

You might also read

Related Articles

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

Sort by
Same author

Foundations of Gerophysics.

Aging·2026
Same author

Aging health dynamics cross a tipping point near age 75.

ArXiv·2026
Same author

Uncovering the multivariate genetic architecture of frailty with genomic structural equation modeling.

Nature genetics·2025
Same author

A complex systems approach to aging biology.

Nature aging·2023
Same author

Network topologies for maximal organismal health span and lifespan.

Chaos (Woodbury, N.Y.)·2023
Same author

Efficient representations of binarized health deficit data: the frailty index and beyond.

GeroScience·2023
Same journal

Therapeutic potential of crude protein extracts from two Egyptian freshwater snails Lanistes carinatus and Bellamya unicolor.

Scientific reports·2026
Same journal

Microbial contamination of donor corneas and post-keratoplasty endophthalmitis: a comparison between Japanese and U.S. eye banks using cold storage.

Scientific reports·2026
Same journal

Prevalence and contributing factors of virological non-suppression among adult patients on first-line antiretroviral therapy in tertiary hospitals in Ethiopia.

Scientific reports·2026
Same journal

An in vitro comparison of color stability between alkasite and different restorative materials in various staining solutions.

Scientific reports·2026
Same journal

Toward accessible mRNA LNP formulation: systematic evaluation of mixing strategies and key parameters.

Scientific reports·2026
Same journal

A network analysis of personality traits, mentalizing, and psychological health in Chinese college students.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Nov 30, 2025

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
08:53

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

1.5K

Generating synthetic aging trajectories with a weighted network model using cross-sectional data.

Spencer Farrell1, Arnold Mitnitski2, Kenneth Rockwood2

  • 1Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada. spencer.farrell@dal.ca.

Scientific Reports
|November 17, 2020
PubMed
Summary
This summary is machine-generated.

We created a computational model simulating human aging by modeling health attributes that decline over time, leading to mortality. This model accurately replicates observed health data and mortality patterns in aging populations.

More Related Videos

Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model
10:58

Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model

Published on: August 29, 2013

11.5K
The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.8K

Related Experiment Videos

Last Updated: Nov 30, 2025

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine
08:53

Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine

Published on: January 26, 2024

1.5K
Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model
10:58

Obtaining Specimens with Slowed, Accelerated and Reversed Aging in the Honey Bee Model

Published on: August 29, 2013

11.5K
The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.8K

Area of Science:

  • Computational biology
  • Gerontology
  • Biostatistics

Background:

  • Human aging is a complex process involving the accumulation of health deficits.
  • Existing models often lack the granularity to capture individual health trajectories.
  • Understanding aging dynamics is crucial for developing targeted health interventions.

Purpose of the Study:

  • To develop a novel computational model of human aging.
  • To simulate individual health trajectories based on interacting health attributes.
  • To validate the model against real-world aging study data.

Main Methods:

  • Developed a weighted network model of interacting health attributes.
  • Simulated stochastic damage to attributes over time, leading to health deficits and mortality.
  • Trained and tested the model using data from two cross-sectional observational aging studies.

Main Results:

  • The model successfully generated synthetic individual aging trajectories.
  • Simulated cohorts closely matched observed health characteristics and mortality patterns.
  • Predicted average health trajectories and survival probabilities showed strong agreement with empirical data.

Conclusions:

  • The computational network model provides a robust framework for understanding human aging.
  • The model can generate realistic synthetic aging data for further research.
  • This approach offers new possibilities for personalized health predictions and interventions.