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Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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

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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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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

Model Approaches for Pharmacokinetic Data: Compartment Models

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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.
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Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional 3D Model
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Quantifying tissue growth, shape and collision via continuum models and Bayesian inference.

Carles Falcó1, Daniel J Cohen2,3, José A Carrillo1

  • 1Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.

Journal of the Royal Society, Interface
|July 19, 2023
PubMed
Summary
This summary is machine-generated.

This study models epithelial tissue self-assembly, finding that population pressure, not random motion, accurately predicts multi-tissue interactions and collisions for tissue engineering applications.

Keywords:
Bayesian inferencecell migrationcontinuum modelidentifiability analysispopulation pressure

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Area of Science:

  • Developmental Biology
  • Biophysics
  • Computational Biology

Background:

  • Biological systems involve complex interactions across scales.
  • Understanding tissue self-assembly is crucial for tissue engineering.

Purpose of the Study:

  • To quantitatively describe the self-assembly of multiple epithelial sheets.
  • To compare continuum models for tissue growth and collision dynamics.

Main Methods:

  • Utilized experimental data, mathematical modeling, and Bayesian parameter inference.
  • Employed two continuum models: random cell motion and population pressure gradients.
  • Calibrated models to reproduce single tissue expansion features.

Main Results:

  • Both models accurately simulated single tissue expansions.
  • Random motion models showed unrealistic behavior in multi-tissue interactions.
  • Population pressure models better matched experimental data for interacting tissues.

Conclusions:

  • Population pressure models are more suitable for predicting multi-tissue dynamics.
  • Tissue shape and pressure significantly influence multi-tissue collisions.
  • This approach aids in designing tissue composites and advancing tissue engineering.