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Related Concept Videos

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...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
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In the case of subcutaneously administered drugs,...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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.
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Related Experiment Video

Updated: Jun 25, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Grammatical Immune System Evolution for reverse engineering nonlinear dynamic Bayesian models.

B A McKinney1, D Tian

  • 1Department of Genetics, University of Alabama School of Medicine, Birmingham, AL 35294, USA. brett.mckinney@gmail.com

Cancer Informatics
|March 5, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces Grammatical Immune System Evolution (GISE), a novel algorithm for evolving nonlinear dynamic models to analyze biomolecular interactions and improve biological pathway modeling.

Keywords:
V(D)J recombinationartificial immune systemdynamic bayesian networkestrogen metabolismnonlinear dynamic bayesian modelsomatic hypermutationunscented kalman filter

Related Experiment Videos

Last Updated: Jun 25, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

  • Computational Biology
  • Artificial Intelligence
  • Systems Biology

Background:

  • Accurate modeling of complex biological systems, such as interacting biomolecules, is crucial for understanding cellular processes.
  • Existing methods may struggle to capture the intricate nonlinear dynamics inherent in biological time-series data.
  • Developing robust algorithms for inferring biological models from experimental data remains a significant challenge.

Purpose of the Study:

  • To introduce and evaluate Grammatical Immune System Evolution (GISE), a grammar-based artificial immune system algorithm.
  • To demonstrate GISE's capability in learning both the structure and parameters of nonlinear dynamic models from time-series data.
  • To apply GISE for inferring an improved kinetic model of 17beta-estradiol oxidative metabolism.

Main Methods:

  • Development of a grammar-based machine learning approach inspired by artificial immune systems.
  • Implementation of in silico immunogenetic mechanisms for generating model-structure diversity and enforcing semantic constraints.
  • Application of GISE to a nonlinear system identification problem using artificial time-series data.
  • Utilizing GISE to infer a kinetic model from experimental data on 17beta-estradiol metabolism.

Main Results:

  • The GISE algorithm successfully evolves nonlinear dynamic models that fit time-series data of interacting biomolecules.
  • The grammar component effectively guides model evolution, ensuring structural integrity and semantic validity.
  • GISE identified an improved kinetic model for the oxidative metabolism of 17beta-estradiol (E(2)) from experimental data.

Conclusions:

  • GISE offers a powerful framework for nonlinear system identification and biological model inference.
  • The integration of grammatical rules with artificial immune systems enhances the evolutionary process for complex dynamic models.
  • This approach advances the ability to model and understand intricate biological pathways from experimental observations.