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Related Concept Videos

The JAK-STAT Signaling Pathway01:20

The JAK-STAT Signaling Pathway

Several cytokine receptors have tightly bound Janus kinase or JAK proteins attached at their cytosolic tail. Small signaling molecules such as cytokines, growth hormones, or prolactins bind to the cytokine receptors and initiate their dimerization. The dimerization brings the cytosolic JAKs together that trans-phosphorylate and activates each other. The activated JAKs now phosphorylate cytosolic tails of the cytokine receptors, which serve as binding sites for adaptor proteins such as  SH2...
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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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...
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
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Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

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Time-resolved Förster Resonance Energy Transfer Assays for Measurement of Endogenous Phosphorylated STAT Proteins in Human Cells
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Time-resolved Förster Resonance Energy Transfer Assays for Measurement of Endogenous Phosphorylated STAT Proteins in Human Cells

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High-dimensional Bayesian parameter estimation: case study for a model of JAK2/STAT5 signaling.

S Hug1, A Raue, J Hasenauer

  • 1Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Germany; Department of Mathematics, Technische Universität München, Germany.

Mathematical Biosciences
|April 23, 2013
PubMed
Summary

This study demonstrates Bayesian parameter estimation for complex ordinary differential equation models. Advanced methods successfully analyzed a high-dimensional biological pathway, enabling better model predictions and experimental design.

Keywords:
Bayesian inferenceCellular signal transduction pathwaysOrdinary differential equation modelsParameter estimationProfile likelihood

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Time-resolved Förster Resonance Energy Transfer Assays for Measurement of Endogenous Phosphorylated STAT Proteins in Human Cells
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Systems Biology
  • Computational Biology
  • Statistical Modeling

Background:

  • Ordinary differential equation (ODE) models are crucial in systems biology for understanding complex biological pathways.
  • Inferring numerous unknown parameters from experimental data in high-dimensional parameter spaces presents significant computational and conceptual challenges.
  • Accurate parameter estimation is essential for robust model validation and reliable prediction of biological system behavior.

Purpose of the Study:

  • To present a detailed Bayesian parameter estimation framework for ODE models with high-dimensional parameter spaces.
  • To address the challenges of statistical inference and uncertainty quantification in complex dynamical systems.
  • To demonstrate the feasibility of rigorous quantitative dynamical modeling in systems biology.

Main Methods:

  • Utilized a profile posterior approach combined with Markov chain Monte Carlo (MCMC) sampling for statistical inference.
  • Applied a multi-chain sampling strategy to handle multimodal posterior distributions, ensuring efficient parameter exploration.
  • Analyzed a specific dynamical model of the Janus Kinase 2 (JAK2)/Signal Transducer and Activator of Transcription 5 (STAT5) signal transduction pathway.

Main Results:

  • Successfully performed Bayesian parameter estimation for a complex model with over one hundred parameters.
  • Identified a bimodal posterior distribution for model parameters using the profile posterior approach.
  • Demonstrated efficient mixing and convergence using a multi-chain MCMC sampling strategy for the identified multimodal distribution.

Conclusions:

  • Bayesian parameter estimation is a feasible and powerful approach for quantitative dynamical modeling in high-dimensional systems biology.
  • The employed statistical methods allow for rigorous assessment of model and prediction uncertainties.
  • This work provides a foundation for designing informative experiments to improve model explanatory power and biological insight.