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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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...
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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...
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...

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Finite Element Modelling of a Cellular Electric Microenvironment
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Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Systems biology and integrative physiological modelling.

Robert L Hester1, Radu Iliescu, Richard Summers

  • 1Department of Physiology, University of Mississippi Medical Centre, Jackson, MS 39216, USA. rhester@umc.edu

The Journal of Physiology
|December 8, 2010
PubMed
Summary
This summary is machine-generated.

Systems biology integrates medicine and computation to understand complex diseases. Developing a comprehensive human body model is crucial for advancing personalized medicine and improving clinical outcomes.

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Area of Science:

  • Systems biology
  • Translational medicine
  • Computational biology

Background:

  • Systems biology integrates biological and medical data with computational approaches.
  • Genomics and proteomics discoveries drive the need for targeted therapeutic strategies.
  • Understanding complex disease aetiologies is key for personalized medicine.

Purpose of the Study:

  • To explore the development of a comprehensive mathematical model of the human body (Human Physiome).
  • To provide a framework for testing hypotheses in clinical outcomes.
  • To review current efforts and challenges in creating a validated Human Model.

Main Methods:

  • Review of existing efforts to develop a Human Physiome.
  • Discussion of the integration of genetics, proteins, cells, organs, and systems.
  • Emphasis on mathematical modeling and computational frameworks.

Main Results:

  • Current data complexity requires systems-level analysis.
  • A validated Human Model is essential for translational medicine.
  • Multiple efforts exist to develop a Human Physiome, each with limitations.

Conclusions:

  • A validated, integrative Human Model is essential for advancing personalized medicine.
  • Collaborative efforts between integrative physiologists and diverse scientific experts are required.
  • Accurate human body modeling will facilitate hypothesis testing and improve clinical outcomes.