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Mechanistic Models: Overview of Compartment Models01:21

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Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
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Multicompartment Models: Overview01:14

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

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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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Typical Model Studies01:30

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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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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Internal and External Forces01:12

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Newton's first law states that a net external force causes a change in motion. External forces act on an object or system, originating outside of the object or system. In contrast, internal forces originate inside the system of interest and do not lead to any acceleration. In simpler words, internal forces are forces that act on one part of an object and are exerted by another part of the same object. External forces are forces that act on an object due to some other object. Therefore, when...
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Related Experiment Video

Updated: Nov 25, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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Bottom-up coarse-grained models for external fields and interfaces.

Michael DeLyser1, W G Noid1

  • 1Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA.

The Journal of Chemical Physics
|December 15, 2020
PubMed
Summary
This summary is machine-generated.

Multiscale coarse-grained (MS-CG) models with local-density (LD) potentials significantly improve the description of interfacial and inhomogeneous systems. These advanced CG models offer better accuracy and transferability for complex fluid interfaces.

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Area of Science:

  • Computational chemistry and materials science
  • Statistical mechanics and thermodynamics
  • Soft matter physics

Background:

  • Bottom-up coarse-grained (CG) models excel in homogeneous systems but struggle with inhomogeneous environments and thermodynamic properties.
  • Accurate modeling of interfaces and complex fluid systems remains a significant challenge for existing CG approaches.
  • Limited transferability and poor thermodynamic descriptions hinder the application of bottom-up CG models to inhomogeneous systems.

Purpose of the Study:

  • To investigate and enhance bottom-up coarse-grained (CG) models for describing interfaces and inhomogeneous systems.
  • To develop and assess multiscale CG (MS-CG) models incorporating local-density (LD) potentials for liquid-vapor interfaces.
  • To connect MS-CG variational principles with established liquid state theory for improved physical insight.

Main Methods:

  • Analysis of external field effects on the many-body potential of mean force.
  • Development of MS-CG models utilizing local-density (LD) potentials.
  • Assessment of MS-CG models for a liquid methanol film adsorbed on an attractive wall in vapor coexistence.

Main Results:

  • The MS-CG variational principle for external fields generalizes the Yvon-Born-Green equation, linking to liquid state theory.
  • Pair-additive potentials yield inadequate accuracy and transferability for interfacial systems.
  • MS-CG models with LD potentials significantly improve accuracy and transferability, accurately describing wall-liquid, liquid-vapor interfaces, bulk liquid density, and vapor phase properties.

Conclusions:

  • Local-density (LD) potentials are crucial for enhancing the accuracy and transferability of bottom-up CG models for interfacial systems.
  • The developed MS-CG model with LD potentials provides a robust framework for accurately predicting structural and thermodynamic properties of both bulk and interfacial fluids.
  • LD potentials show considerable promise for creating broadly applicable and accurate CG models for complex molecular systems.