Related Experiment Video
Updated: Jun 22, 2026
08:12
Experimental Methods to Study Human Postural Control
Published on: September 11, 2019
Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile
1Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com
Bioinformatics (Oxford, England)
|June 10, 2009
Summary
This study introduces a profile likelihood approach to assess how well biological model parameters are determined by experimental data. It helps identify non-identifiable parameters and derive confidence intervals for better model reduction and experimental design.
Area of Science:
- Systems Biology
- Computational Biology
- Mathematical Modeling
Background:
- Biological reaction networks are often modeled using differential equations, leading to complex systems.
- Parameter calibration aims to fit models to experimental data, but model identifiability is crucial for reliable predictions.
- Identifiability analysis is essential to understand how well model parameters are constrained by data.
Purpose of the Study:
- To develop and present a novel approach for analyzing the identifiability of parameters in biological models.
- To detect both structural and practical non-identifiabilities in complex biological systems.
- To provide a method for deriving confidence intervals for model parameters.
Main Methods:
- The study utilizes the profile likelihood method to analyze model parameter identifiability.
- This approach allows for the detection of functionally related parameters causing structural non-identifiabilities.
- It also identifies practical non-identifiabilities arising from data limitations.
Main Results:
- The profile likelihood approach effectively detects structural non-identifiabilities in biological models.
- Practical non-identifiabilities, influenced by data quantity and quality, are also identified.
- Confidence intervals for model parameters can be reliably derived using this method.
Conclusions:
- The proposed method offers interpretable results for assessing model parameter identifiability.
- Findings facilitate informed experimental planning and effective model reduction strategies.
- The approach enhances the reliability of biological model predictions and interpretations.
Related Concept Videos
01:23Mechanistic 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)...
01:29Mechanistic 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...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
01:21Mechanistic 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...
01:25Modeling with Differential Equations
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
01:24One-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...
On...
01:06Model 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...
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...