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Related Concept Videos

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: 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)...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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...

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo.

Andrew Golightly1, Darren J Wilkinson

  • 1School of Mathematics and Statistics, Newcastle University, Merz Court, Newcastle upon Tyne NE1 7RU, UK.

Interface Focus
|December 11, 2012
PubMed
Summary

This study introduces particle Markov chain Monte Carlo methods for parameter inference in complex stochastic kinetic models. These computational systems biology approaches effectively handle uncertain parameters in biological process modeling.

Keywords:
Markov jump processchemical Langevin equationpseudo-marginal approachsequential Monte Carlostochastic differential equationstochastic kinetic model

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Area of Science:

  • Computational systems biology
  • Biophysics
  • Biomathematics

Background:

  • Mechanistic models of biological processes are crucial but often stochastic and analytically intractable.
  • Estimating uncertain parameters from time-course data is a key challenge in computational systems biology.

Purpose of the Study:

  • To develop and apply effective methods for inferring parameters of stochastic kinetic models.
  • To investigate the utility of particle Markov chain Monte Carlo (pMCMC) algorithms for this inference task.

Main Methods:

  • Utilized particle Markov chain Monte Carlo (pMCMC) algorithms for parameter inference.
  • Explored approximations based on stochastic differential equations (SDEs) and improved inference schemes.
  • Applied the methodology to Lotka-Volterra and prokaryotic auto-regulatory network models.

Main Results:

  • pMCMC algorithms proved to be a computationally intensive yet effective approach for parameter inference.
  • SDE-based approximations and structural improvements enhanced the inference scheme's performance.
  • Successful application to biological network models demonstrated the methodology's practical utility.

Conclusions:

  • Particle Markov chain Monte Carlo methods offer a powerful computational approach for parameter inference in complex stochastic biological models.
  • The developed methodology, including SDE approximations, is applicable to real-world biological systems like gene regulatory networks.