Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Kaplan-Meier Approach

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

The one-compartment open model is a simplified approach used in pharmacokinetics to understand the distribution and elimination of a drug administered through an intravenous bolus. This model assumes rapid drug dispersal throughout the body and elimination using a first-order process. Key pharmacokinetic parameters, such as the elimination rate constant (k), half-life (t1/2), and the apparent volume of distribution (Vd), can be estimated from this model. The elimination rate is calculated from...

Estimating Population Mean with Unknown Standard Deviation

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...

You might also read

Related Articles

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

Sort by

Same author

Emergency Department Food Insecurity Screening, Food Voucher Distribution and Utilization: A Prospective Cohort Study.

The western journal of emergency medicine·2024

Same author

COVID-19 preparedness and response in the Pitcairn Islands: keeping one of the world's smallest and most isolated populations safe in a pandemic.

Western Pacific surveillance and response journal : WPSAR·2024

Same author

Bayesian multivariant fine mapping using the Laplace prior.

Genetic epidemiology·2023

Same author

"It's so Natural It Goes Hand in Hand": A Qualitative Study of Maternal Perceptions of Cannabis Use during Pregnancy.

Journal of psychoactive drugs·2022

Same author

Corrigendum to: Probabilistic identification of bacterial essential genes via insertion density using TraDIS data with Tn5 libraries.

Bioinformatics (Oxford, England)·2021

Same author

Probabilistic identification of bacterial essential genes via insertion density using TraDIS data with Tn5 libraries.

Bioinformatics (Oxford, England)·2021

Search research articles

Related Experiment Video

Updated: May 22, 2026

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Published on: December 13, 2012

Parameter estimation for an immortal model of colonic stem cell division using approximate Bayesian computation.

Kevin Walters¹

¹School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, United Kingdom. k.walters@sheffield.ac.uk

Journal of Theoretical Biology

|May 5, 2012

Summary

This study estimates parameters for colonic stem cell division using DNA methylation patterns. Findings challenge site-independent models and suggest asymmetric division may involve over 8 stem cells.

More Related Videos

Quantification of Colonic Stem Cell Mutations

Quantification of Colonic Stem Cell Mutations

Published on: September 25, 2015

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Related Experiment Videos

Last Updated: May 22, 2026

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes

Published on: December 13, 2012

Quantification of Colonic Stem Cell Mutations

Quantification of Colonic Stem Cell Mutations

Published on: September 25, 2015

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

Area of Science:

Computational biology
Genetics
Stem cell research

Background:

DNA methylation patterns serve as a molecular clock for colonic stem cell turnover.
Debate exists between symmetric (immortal) and asymmetric stem cell division models.
Previous models often overlooked methyltransferase processivity and methylation errors in differentiated cells.

Purpose of the Study:

To estimate parameters in an immortal model of colonic stem cell division.
To address limitations in previous models by incorporating neighbor-dependent methylation error rates.
To investigate the implications of DNA methylation patterns on stem cell division models.

Main Methods:

Utilized approximate Bayesian computation (ABC) for parameter estimation.
Inferred parameters from DNA methylation patterns observed in human colon cells.
Developed algebraic expressions for summary statistics accounting for cell division variation.

Main Results:

Accurate parameter estimates were obtained for a neighbor-dependent methylation error model.
Results suggest asymmetric division may involve more than 8 stem cells maintaining colon stem cell niches.
Provided evidence against site-independent models for methylation errors and indicated potential for preferential strand segregation.

Conclusions:

Approximate Bayesian computation is effective for analyzing complex, neighbor-dependent methylation models.
The findings contribute to understanding stem cell dynamics and the mechanisms of colon cancer development.
The study refines models of stem cell turnover by accounting for biological complexities in methylation processes.