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Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Multicompartmental models are crucial tools in pharmacokinetics, providing a framework to understand how drugs move within the body. The two-compartment model is a crucial subtype, segmenting the body into central and peripheral compartments. The central compartment represents areas with high blood flow, such as plasma and highly perfused organs like the kidneys and liver, while the peripheral compartment signifies tissues with lower blood flow, like adipose tissue and muscle tissue.
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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Modeling Site-and-Branch-Heterogeneity with GFmix.

Charley G P McCarthy1,2, Edward Susko1,3, Ryo Harada1,2

  • 1Institute for Comparative Genomics, Dalhousie University, Halifax, NS B3H 4R2, Canada.

Systematic Biology
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Improved phylogenetic models (GFmix) accurately infer evolutionary relationships by accounting for amino acid compositional heterogeneity. Enhanced GFmix models offer greater accuracy and flexibility in phylogenetic inference, reducing artefacts in evolutionary studies.

Keywords:
Compositional heterogeneityMixture modelsPhylogeneticsSimulation

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Area of Science:

  • Evolutionary biology
  • Bioinformatics
  • Computational phylogenetics

Background:

  • Phylogenetic trees are inferred from protein sequences, but compositional heterogeneity can cause artefacts.
  • Existing models struggle with computational cost and limited scope for compositional variation.

Purpose of the Study:

  • To investigate and improve the GFmix model for phylogenetic inference.
  • To enhance accuracy and computational efficiency in modeling compositional heterogeneity.

Main Methods:

  • Developed improved GFmix models with fewer constraints and user-defined heterogeneity.
  • Implemented full maximum-likelihood optimization for parameters.
  • Created new methods for detecting compositional heterogeneity.

Main Results:

  • Improved GFmix models accurately estimate branch-specific compositions and lengths in heterogeneous trees.
  • The most complex GFmix model consistently supported the correct tree with improved likelihoods on real data.
  • New methods effectively detect compositional heterogeneity.

Conclusions:

  • Enhanced GFmix models provide more accurate and robust phylogenetic inference.
  • The improved models overcome limitations of previous versions, particularly for deep divergences.
  • These advancements reduce phylogenetic artefacts caused by compositional variation.