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

124
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...
124
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

124
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
124
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

74.9K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
74.9K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

166
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
166
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

155
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...
155
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

154
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...
154

You might also read

Related Articles

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

Sort by
Same author

Correction: "Distinguishing Phylogenetic Level-2 Networks with Quartets and Inter-Taxon Quartet Distances".

Bulletin of mathematical biology·2026
Same author

Distinguishing Phylogenetic Level-2 Networks with Quartets and Inter-Taxon Quartet Distances.

Bulletin of mathematical biology·2025
Same author

Beyond Level-1: Identifiability of a Class of Galled Tree-Child Networks.

Bulletin of mathematical biology·2025
Same author

NANUQ<sup>+</sup>: A divide-and-conquer approach to network estimation.

Algorithms for molecular biology : AMB·2025
Same author

TINNiK: inference of the tree of blobs of a species network under the coalescent model.

Algorithms for molecular biology : AMB·2024
Same author

Identifiability of Level-1 Species Networks from Gene Tree Quartets.

Bulletin of mathematical biology·2024

Related Experiment Video

Updated: Nov 6, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.3K

Parameter Identifiability for a Profile Mixture Model of Protein Evolution.

Samaneh Yourdkhani1, Elizabeth S Allman1, John A Rhodes1

  • 1Department of Mathematics and Statistics, University of Alaska Fairbanks, Fairbanks, Alaska, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 7, 2021
PubMed
Summary

Profile mixture (PM) models analyze protein evolution by considering varied amino acid distributions across sequence sites. This study proves PM model parameters are identifiable for practical protein evolution analyses with sufficient taxa and limited profiles.

Keywords:
parameter identifiabilityphylogenetic treesprofile mixture model

More Related Videos

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.2K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

Related Experiment Videos

Last Updated: Nov 6, 2025

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.3K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

2.2K
Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

7.4K

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Profile mixture (PM) models are used in protein evolution studies.
  • These models assume varied amino acid distributions (profiles) across sequence sites on an evolutionary tree.
  • Parameter identifiability is crucial for validating stochastic models.

Purpose of the Study:

  • To determine if profile mixture model parameters are identifiable from the resulting probability distribution.
  • To establish conditions under which PM models are suitable for empirical data analysis.

Main Methods:

  • Utilized algebraic methods to analyze parameter identifiability.
  • Investigated the relationship between tree topology, number of taxa, number of profiles, and parameter identifiability.

Main Results:

  • Demonstrated that PM model parameters are identifiable under practical conditions.
  • Specifically, for evolutionary trees with 9 or more taxa, both tree topology and numerical parameters are identifiable when the number of profiles is less than 74.

Conclusions:

  • The study confirms the identifiability of parameters for profile mixture models.
  • This finding supports the use of PM models in empirical analyses of protein sequence data, particularly in evolutionary biology research.