Jove
Visualize
Contact Us

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

310
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...
310
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.7K
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...
5.7K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

321
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...
321
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

3.1K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
3.1K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

5.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
5.2K
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

1.4K
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...
1.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Temporal trends and spatial variations of total mercury in sediments of the legacy-contaminated river Elbe (Germany).

Environmental monitoring and assessment·2025
Same author

Future climate and demographic changes will almost double the risk of schistosomiasis transmission in the Lake Victoria Basin.

One health (Amsterdam, Netherlands)·2025
Same author

Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia.

Scientific reports·2025
Same author

Modelling Temperature-dependent Schistosomiasis Dynamics for Single and Co-infections with S. mansoni and S. haematobium.

PloS one·2025
Same author

The older, the better: a comprehensive survey of soil organic carbon under commercial oil palm plantations.

Environmental monitoring and assessment·2024
Same author

Assessing the potential of nature-based solutions as sustainable land and water management strategies in the high tropical Andean páramo ecosystem.

Journal of environmental management·2024
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Mar 28, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K

SPOTting Model Parameters Using a Ready-Made Python Package.

Tobias Houska1, Philipp Kraft1, Alejandro Chamorro-Chavez1

  • 1Institute for Landscape Ecology and Resources Management, Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University, Giessen, Germany.

Plos One
|December 19, 2015
PubMed
Summary

SPOTPY is an open-source Python package for ecological model parameter optimization. It offers various algorithms and objective functions, simplifying complex model calibration and analysis for researchers.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics
13:30

Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics

Published on: February 18, 2022

5.1K

Related Experiment Videos

Last Updated: Mar 28, 2026

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics
13:30

Mass-Sensitive Particle Tracking to Characterize Membrane-Associated Macromolecule Dynamics

Published on: February 18, 2022

5.1K

Area of Science:

  • Environmental modeling
  • Computational science
  • Ecological informatics

Background:

  • Parameter estimation in ecological models is often limited by tool availability rather than performance.
  • A need exists for a unified, flexible tool to handle diverse model calibration and analysis requirements.

Purpose of the Study:

  • To introduce SPOTPY (Statistical Parameter Optimization Tool), an open-source Python package.
  • To provide a comprehensive suite of methods for calibrating, analyzing, and optimizing parameters in ecological models.
  • To demonstrate the package's versatility and effectiveness across various case studies.

Main Methods:

  • SPOTPY integrates eight widely used optimization algorithms, 11 objective functions, and eight parameter distributions.
  • The package features a model-independent structure, enabling parallel processing via Message Passing Interface (MPI).
  • Five diverse case studies were used for testing, including function optimization and soil moisture/biogeochemistry model calibration.

Main Results:

  • SPOTPY facilitates model parameter optimization with minimal coding effort.
  • The package successfully parameterized complex functions and ecological models across different objective functions.
  • Case studies confirmed that a single package with multiple methods enhances optimization power, as no single method is universally optimal.

Conclusions:

  • SPOTPY offers a powerful, flexible, and accessible solution for ecological model parameter optimization.
  • The availability of diverse algorithms and objective functions within SPOTPY addresses the limitations of method availability.
  • The package's model-independent design and parallel processing capabilities support its application on various computational platforms.