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

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

276
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...
276
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

569
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
569
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

249
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...
249
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

541
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
541
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

334
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
334
Molecular Models02:00

Molecular Models

43.7K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
43.7K

You might also read

Related Articles

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

Sort by
Same author

Statistical and Structural Bias in Birth-Death Models.

Bulletin of mathematical biology·2026
Same author

Unified theory resolves phenological paradoxes in biocollection data by modeling phenophase duration during Bayesian inference.

The New phytologist·2026
Same author

Gestation length both shapes and is shaped by other life history traits in terrestrial eutherian mammals.

Evolution letters·2025
Same author

Longevity in plants impacts phylogenetic and population dynamics.

The New phytologist·2025
Same author

The Evolution of Male Weapons Is Associated with the Type of Breeding Site in a Clade of Neotropical Frogs.

Integrative zoology·2025
Same author

Longevity in plants impacts phylogenetic and population dynamics.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Feb 5, 2026

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

1.0K

Hidden state models improve state-dependent diversification approaches, including biogeographical models.

Daniel S Caetano1, Brian C O'Meara2, Jeremy M Beaulieu1

  • 1Department of Biological Sciences, University of Arkansas, Fayetteville, Arkansas, 72701.

Evolution; International Journal of Organic Evolution
|September 19, 2018
PubMed
Summary

Hidden Markov models (HMMs) offer a solution to issues with state-dependent speciation and extinction (SSE) models. This study clarifies "hidden states" in HMMs, improving diversification analyses.

Keywords:
BiSSEBiogeographyGeoSSEHiSSEhidden Markovmodel averaging

More Related Videos

Cardiopulmonary Bypass in a Mouse Model: A Novel Approach
08:08

Cardiopulmonary Bypass in a Mouse Model: A Novel Approach

Published on: September 22, 2017

11.6K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K

Related Experiment Videos

Last Updated: Feb 5, 2026

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics
10:23

Author Spotlight: Computing the Effects of a Local Radiofrequency Hyperthermia Intervention on Tumor Biomechanics

Published on: December 1, 2023

1.0K
Cardiopulmonary Bypass in a Mouse Model: A Novel Approach
08:08

Cardiopulmonary Bypass in a Mouse Model: A Novel Approach

Published on: September 22, 2017

11.6K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K

Area of Science:

  • Evolutionary Biology
  • Computational Biology
  • Phylogenetics

Background:

  • State-dependent speciation and extinction (SSE) models face criticism for high false positive rates.
  • Nonparametric and semiparametric approaches are suggested alternatives, but hidden Markov models (HMMs) offer a partial solution.
  • HMMs can address rate heterogeneity in phylogenies by incorporating 'hidden states'.

Purpose of the Study:

  • To clarify the interpretation of 'hidden states' within HMMs for diversification analyses.
  • To demonstrate how HMMs with model-averaging account for hidden traits when assessing diversification drivers.
  • To extend HMMs to geographic SSE models, creating the GeoHiSSE framework.

Main Methods:

  • Utilized hidden Markov modeling (HMM) to incorporate unobserved states influencing diversification.
  • Employed model-averaging techniques to integrate HMMs with trait-dependent diversification models.
  • Extended HMMs to the geographic state-dependent speciation and extinction (GeoSSE) model, developing GeoHiSSE.

Main Results:

  • Clarified that 'hidden states' in HMMs represent unobserved factors influencing diversification.
  • Showed HMMs combined with model-averaging effectively account for hidden traits impacting diversification.
  • Demonstrated the efficacy of the GeoHiSSE extension through simulations and an empirical dataset.

Conclusions:

  • Hidden states provide a general framework for distinguishing heterogeneous diversification effects.
  • HMMs enhance the reliability of detecting state-dependent diversification shifts.
  • The GeoHiSSE model offers a robust extension for analyzing geographic diversification patterns.