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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 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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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...
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: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...

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Related Experiment Video

Updated: Jun 10, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Parameter inference for discretely observed stochastic kinetic models using stochastic gradient descent.

Yuanfeng Wang1, Scott Christley, Eric Mjolsness

  • 1Department of Physics and Astronomy, University of California, Irvine, 92617, USA.

BMC Systems Biology
|July 29, 2010
PubMed
Summary
This summary is machine-generated.

We developed an efficient algorithm for parameter inference in stochastic kinetic models using stochastic gradient descent (SGD). This method accurately estimates kinetic parameters from discrete time-course data for various biochemical reaction models.

Related Experiment Videos

Last Updated: Jun 10, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Computational Systems Biology
  • Biochemical Reaction Modeling
  • Statistical Inference

Background:

  • Stochastic models are crucial for systems with small populations, particularly in computational systems biology.
  • Analyzing stochastic models is computationally intensive, lagging behind deterministic methods.
  • Efficient algorithms are needed for parameter inference in stochastic kinetic models.

Purpose of the Study:

  • To develop an efficient algorithm for parameter inference in stochastic kinetic models.
  • To handle discrete time-course observations of molecular species.
  • To address the computational challenges in analyzing stochastic models.

Main Methods:

  • Maximum likelihood estimation using stochastic gradient descent (SGD).
  • Derivation of a general formula for the likelihood function gradient.
  • Reversible jump Markov chain Monte Carlo (RJMCMC) sampling for gradient estimation.
  • Flux balance analysis for automatic construction of reversible jump samplers.

Main Results:

  • An algorithm for kinetic rate parameter inference based on SGD.
  • Successful application to birth-death and auto-regulatory gene network models.
  • Good agreement between inferred and actual parameters in test models.
  • RJMCMC algorithms provided for fully and partially observed data.

Conclusions:

  • The proposed SGD method offers a general and efficient framework for parameter inference in stochastic kinetic models.
  • The method is effective for both partially and fully observed systems.
  • Automatic sampler construction and general gradient formulation enhance applicability to diverse models.
  • Derivations support sensitivity analysis and Bayesian inference; software is publicly available.