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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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

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...
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...

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Related Experiment Video

Updated: Jun 27, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Clustering models.

Wolfgang W A Schamel1, Michael Reth

  • 1Department of Molecular Immunology, Max Planck-Institute for Immunobiology and Faculty of Biology, University of Freiburg, Stuebeweg 51, 79108 Freiburg, Germany. schamel@immunbio.mpg.de

Advances in Experimental Medicine and Biology
|December 11, 2008
PubMed
Summary
This summary is machine-generated.

Multichain immune recognition receptors (MIRRs) activate lymphocytes upon ligand binding. This review explores models of how ligand binding transmits signals across the cell membrane, focusing on receptor clustering and conformational changes.

Related Experiment Videos

Last Updated: Jun 27, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Immunology
  • Cell Signaling
  • Molecular Biology

Background:

  • Multichain immune recognition receptors (MIRRs) mediate lymphocyte activation upon ligand binding.
  • The precise mechanism of signal transmission from ligand binding to intracellular activation remains unclear.
  • Existing models involve receptor clustering, conformational changes, or segregation of signaling molecules.

Purpose of the Study:

  • To review and discuss current models of multichain immune recognition receptor (MIRR) triggering.
  • To elucidate the initial steps of signal communication across the plasma membrane following ligand binding.
  • To focus on specific models including homodustering, heterodustering, and the pseudodimer model.

Main Methods:

  • Literature review of existing models for MIRR triggering.
  • Analysis of proposed mechanisms including receptor clustering, conformational changes, and molecular segregation.
  • Focus on homodustering, heterodustering, and pseudodimer models.

Main Results:

  • Ligand binding can induce receptor clustering, leading to kinase recruitment and phosphorylation.
  • Alternative models suggest ligand binding causes conformational receptor changes or alters the distribution of signaling enzymes.
  • Specific models discussed are homodustering, heterodustering, and the pseudodimer model.

Conclusions:

  • Understanding MIRR triggering is crucial for comprehending lymphocyte activation.
  • Multiple models exist to explain signal transduction initiated by ligand-receptor interactions.
  • Further research is needed to definitively establish the primary mechanism of MIRR activation.