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Related Concept Videos

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)...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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...
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...
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...
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...

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Pattern-oriented modelling: a 'multi-scope' for predictive systems ecology.

Volker Grimm1, Steven F Railsback

  • 1Department of Ecological Modelling, Helmholtz Centre for Environmental Research - UFZ, Permoserstrasse 15, 04318 Leipzig, Germany. volker.grimm@ufz.de

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|December 7, 2011
PubMed
Summary
This summary is machine-generated.

Pattern-oriented modelling (POM) uses observed patterns to build complex ecological models. This rigorous approach improves model accuracy for predicting ecological system responses to change.

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Area of Science:

  • Ecology
  • Ecological Modelling
  • Systems Ecology

Background:

  • Predictive ecological science requires integrated models linking individual behavior to population and ecosystem dynamics across scales.
  • Pattern-oriented modelling (POM) offers a strategy for developing such complex system models.

Purpose of the Study:

  • To outline the principles and application of pattern-oriented modelling (POM) in ecology.
  • To demonstrate how explicit and rigorous use of patterns can enhance ecological model development.

Main Methods:

  • POM involves multi-criteria design, selection, and calibration of models based on observed patterns.
  • Patterns identified at multiple scales guide model structure, submodel selection (e.g., adaptive behavior), and parameterization.
  • A mini-review of POM applications was conducted to assess its utility.

Main Results:

  • Explicit and rigorous pattern utilization in POM facilitates the development of ecologically relevant models.
  • POM aids in determining necessary model components (scales, entities, processes) and selecting appropriate submodels.
  • The approach supports finding accurate parameter values for model calibration.

Conclusions:

  • Pattern-oriented modelling is a robust strategy for building predictive ecological models.
  • Refining POM enhances the complexity and accuracy of models for understanding and predicting ecological responses.
  • This approach is crucial for advancing ecological science towards greater predictability.