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Related Concept Videos

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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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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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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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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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

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

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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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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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Bayesian state space models for dynamic genetic network construction across multiple tissues.

Yulan Liang, Arpad Kelemen

    Statistical Applications in Genetics and Molecular Biology
    |June 26, 2016
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    Summary
    This summary is machine-generated.

    This study introduces dynamic Hierarchical Bayesian state space models to analyze gene-gene interactions and genomic changes during disease treatments. These models effectively capture dynamic genomic profiles and network alterations in response to corticosteroid treatment.

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    Area of Science:

    • Genomics
    • Systems Biology
    • Computational Biology

    Background:

    • Inferring gene-gene interaction networks and pathways is crucial for understanding complex diseases.
    • Estimating dynamic temporal correlations and non-stationarity in genomic data presents a significant challenge.

    Purpose of the Study:

    • To develop dynamic state space models with hierarchical Bayesian settings for inferring dynamic genomic profiles and genetic networks.
    • To address challenges in estimating temporal correlations and non-stationarity in genomic time course data.

    Main Methods:

    • Developed dynamic state space models with time-variant transition and observation matrices.
    • Incorporated temporal correlation structures in multivariate Bayesian state space models.
    • Utilized Hierarchical Bayesian approaches with Monte Carlo Markov Chain and Gibbs sampling for parameter and hidden state variable estimation.

    Main Results:

    • The proposed models successfully captured genomic changes over time and gene-gene interactions in response to corticosteroid (CS) treatment.
    • Analysis of Affymetrix time course data from liver, skeletal muscle, and kidney tissues demonstrated model efficacy.
    • Both simulation and real data analyses confirmed the models' ability to represent dynamic genomic responses.

    Conclusions:

    • Dynamic Hierarchical Bayesian state space models offer a robust framework for analyzing time-varying genomic data.
    • These models can be extended to large-scale genomic data, including next-generation sequencing (NGS) and electronic health records (EHR).
    • The approach facilitates network-based analysis for precision medicine, improving diagnostics and patient outcomes.