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 Experiment Videos

Data-based identifiability analysis of non-linear dynamical models.

S Hengl1, C Kreutz, J Timmer

  • 1Physics Institute, University of Freiburg, Hermann Herder Strasse 3, 79104 Freiburg i.Br., Germany. hengl@fdm.uni-freiburg.de

Bioinformatics (Oxford, England)
|July 31, 2007
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Metrics of left ventricular active relaxation reflect proteomic myocardial remodelling and reverse remodelling.

ESC heart failure·2026
Same author

Culturomics: Deciphering the Microbial Dark Matter in the Oral Cavity.

Journal of dental research·2025
Same author

Investigating two mobile just-in-time adaptive interventions to foster psychological resilience: research protocol of the DynaM-INT study.

BMC psychology·2023
Same author

Expression ratio of the TGFβ-inducible gene MYO10 is prognostic for overall survival of squamous cell lung cancer patients and predicts chemotherapy response.

Scientific reports·2018
Same author

Chemical synthesis and NMR spectroscopy of long stable isotope labelled RNA.

Chemical communications (Cambridge, England)·2017
Same author

Partial break in tolerance of NKG2A<sup>-</sup>/LIR-1<sup>-</sup> single KIR<sup>+</sup> NK cells early in the course of HLA-matched, KIR-mismatched hematopoietic cell transplantation.

Bone marrow transplantation·2017
Same journal

3DICE: Interpretable 3D Cross-Modal Learning for Drug-Target Interaction Prediction and Large-Scale Drug Discovery.

Bioinformatics (Oxford, England)·2026
Same journal

KASSPer: Kinase Active Site Structure Prediction using Protein and Ligand Language Models and Its Application to Virtual Screening.

Bioinformatics (Oxford, England)·2026
Same journal

IDR searcher: a search engine solution for public image resources.

Bioinformatics (Oxford, England)·2026
Same journal

KCFtools: Rapid alignment-free method for introgression screening and GWAS using k-mer profiles.

Bioinformatics (Oxford, England)·2026
Same journal

Meta2DB: Curated shotgun metagenomic feature sets and metadata for health state prediction.

Bioinformatics (Oxford, England)·2026
Same journal

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

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

This study introduces a new method to find and fix non-identifiable parameters in biological models, improving model accuracy. The approach successfully reduces parameter variability, enhancing the reliability of mathematical modeling in systems biology.

Area of Science:

  • Systems Biology
  • Computational Biology
  • Mathematical Biology

Background:

  • Mathematical modeling is crucial for understanding complex biological systems.
  • Non-identifiable parameters pose a significant challenge in model development and interpretation.
  • Detecting functionally related parameters is difficult and hinders accurate model calibration.

Purpose of the Study:

  • To develop a robust method for identifying non-identifiable parameters in mathematical models of biological systems.
  • To provide a tool for testing parameter identifiability, including linear and non-linear relationships.
  • To improve the reliability and accuracy of biological models by addressing parameter ambiguity.

Main Methods:

  • A non-parametric bootstrap-based algorithm termed the method of mean optimal transformations.

Related Experiment Videos

  • Utilizes optimal transformations estimated via the alternating conditional expectation (ACE) algorithm.
  • Incorporates data quality by fitting models to data and analyzing parameter variability.

    • Main Results:

    • The method identifies linear and non-linear parameter relations of any complexity without prior knowledge.
    • It successfully detects independent and thus identifiable parameters.
    • Demonstrated a significant reduction in parameter value variability from 81% to 1% in a realistic dynamical model after addressing non-identifiabilities.

    Conclusions:

    • The method of mean optimal transformations offers a powerful solution for parameter identifiability testing in complex biological models.
    • Accurate identification and fixation of non-identifiable parameters substantially improve model precision.
    • This approach enhances the utility of mathematical modeling for investigating cellular processes and dynamic interactions.