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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.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
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...
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...
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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...

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Related Experiment Videos

Multicommunity weight-driven bipartite network model.

H Fan1, Z Wang, T Ohnishi

  • 1Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo 113-8656, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 15, 2008
PubMed
Summary
This summary is machine-generated.

We modeled rewiring in multicommunity weight-driven bipartite networks (MCWBN). Production scale dynamics depend solely on initial conditions, not producer size distribution.

Related Experiment Videos

Area of Science:

  • Complex Networks
  • Network Science
  • Bipartite Networks

Background:

  • Community structure and rewiring are prevalent in complex networks, especially bipartite ones.
  • Multicommunity weight-driven bipartite networks (MCWBN) offer a framework to study these phenomena.

Purpose of the Study:

  • To develop a model for the degree distribution in the rewiring of MCWBNs.
  • To analyze the production scale dynamics and producer size distribution within these networks.

Main Methods:

  • Constructed a model for MCWBNs with interconnected communities, each containing a bipartite graph.
  • Defined producer and consumer nodes and used a weight matrix for intercommunity trade barriers.
  • Analyzed the nonlinear dynamics of the total producer size (scale of production) per community.

Main Results:

  • The scale of production in each community is determined solely by its initial scale, irrespective of producer size distribution.
  • Intracommunity preferential attachment is higher than intercommunity attachment due to trade barriers.
  • At equilibrium, producer size distribution in MCWBNs mirrors that of single-community models.

Conclusions:

  • The model captures essential features of rewiring in structured bipartite networks.
  • Production scale dynamics are robust to variations in producer size distribution.
  • Equilibrium states simplify the understanding of producer size distribution in complex community structures.