Jove
Visualize
Contact Us
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 Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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...
48
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
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...
36

You might also read

Related Articles

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

Sort by
Same author

Data-driven model reveals increased stability of CAG-expanded huntingtin RNA due to MID1 binding.

PLoS computational biology·2026
Same author

PEtab-GUI: a graphical user interface to create, edit, and inspect PEtab parameter estimation problems.

Bioinformatics (Oxford, England)·2026
Same author

Latent transition analysis for longitudinal studies of post-acute infection syndromes.

Nature communications·2026
Same author

Shiny-Calorie: a context-aware application for indirect calorimetry data analysis and visualization using R.

Bioinformatics advances·2026
Same author

Persistent Low-Grade Inflammation and Post-COVID Condition: Evidence from the ORCHESTRA Cohort.

Biomedicines·2026
Same author

Cholesterol-lowering effects of oats induced by microbially produced phenolic metabolites in metabolic syndrome: a randomized controlled trial.

Nature communications·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 22, 2025

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.1K

Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data.

Domagoj Dorešić1,2, Stephan Grein1, Jan Hasenauer1,2,3

  • 1Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany.

Bioinformatics (Oxford, England)
|June 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a spline-based method to integrate semi-quantitative biological data into dynamical model parameter estimation. The approach reliably discovers unknown measurement transformations and improves parameter inference accuracy.

More Related Videos

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
11:19

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Published on: July 3, 2017

8.2K
Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
14:09

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

15.6K

Related Experiment Videos

Last Updated: Jun 22, 2025

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells
10:21

Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

Published on: September 16, 2020

6.1K
ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth
11:19

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Published on: July 3, 2017

8.2K
Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
14:09

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

15.6K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Quantitative dynamical models are crucial for understanding biological processes.
  • Parameter estimation from experimental data is key, but data is often semi-quantitative.
  • Semi-quantitative data involves nonlinear transformations of the system's state, complicating model comparison.

Purpose of the Study:

  • To develop a versatile method for integrating semi-quantitative data into parameter estimation for dynamical models.
  • To enable comparison between model simulations and semi-quantitative experimental data, even with unknown transformations.
  • To improve the accuracy and efficiency of parameter inference in biological models.

Main Methods:

  • A spline-based approach for integrating diverse semi-quantitative data.
  • Derivation of analytical formulas for hierarchical objective function gradients.
  • Implementation within the open-source Python Parameter Estimation Toolbox (pyPESTO).

Main Results:

  • The method substantially increases parameter estimation efficiency.
  • It reliably discovers unknown nonlinear measurement transformations.
  • It significantly improves parameter inference compared to existing methods for semi-quantitative data.

Conclusions:

  • The proposed spline-based method offers a robust solution for utilizing semi-quantitative data in dynamical modeling.
  • This approach enhances the understanding and prediction of biological systems.
  • The open-source implementation facilitates widespread adoption by modelers.