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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
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...

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Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
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Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Published on: April 23, 2021

Systems biology--biomedical modeling.

Eric A Sobie1, Young-Seon Lee, Sherry L Jenkins

  • 1Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, New York, NY 10029, USA. eric.sobie@mssm.edu

Science Signaling
|September 16, 2011
PubMed
Summary
This summary is machine-generated.

Researchers developed a new course to teach computational approaches for analyzing complex biological systems. This training is crucial for understanding system behaviors and their alterations under changing conditions in biomedical sciences.

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Area of Science:

  • Systems biology
  • Computational biology
  • Bioinformatics

Background:

  • Biological systems are inherently complex, requiring advanced analytical methods.
  • Global analysis and computational approaches are essential for understanding system behaviors and responses to change.

Purpose of the Study:

  • To introduce first-year graduate students to computational principles and approaches for biological systems analysis.
  • To bridge the gap between biological details and quantitative methods for hypothesis generation.

Main Methods:

  • Development of a specialized course at Mount Sinai School of Medicine.
  • Curriculum focused on computational principles and quantitative approaches relevant to biomedical research.

Main Results:

  • The course successfully introduces graduate students to essential computational tools for biological research.
  • Positive reception and anticipation of widespread adoption of such training.

Conclusions:

  • Computational approaches are vital for modern biological and biomedical research.
  • Courses integrating computational methods should become a core component of graduate programs in the life sciences.