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

You might also read

Related Articles

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

Sort by
Same author

Using PyBioNetFit to leverage qualitative and quantitative data in biological model parameterization and uncertainty quantification.

Frontiers in immunology·2026
Same author

Polarized Phase-Sensitive Fluorescence-Image Correlation Spectroscopy.

Biomolecules·2026
Same author

Photoluminescence of Rhodamine from Nano-Confinement Inside 3D Sculptured Coatings.

Nanomaterials (Basel, Switzerland)·2026
Same author

Phase II Trial of Vemurafenib and Sorafenib Combination in Advanced <i>KRAS</i>-Mutated Metastatic Pancreatic Cancer.

Journal of immunotherapy and precision oncology·2026
Same author

Using PyBioNetFit to Leverage Qualitative and Quantitative Data in Biological Model Parameterization and Uncertainty Quantification.

ArXiv·2025
Same author

Pathogenic variants in TMEM184B cause a neurodevelopmental syndrome associated with alteration of metabolic signaling.

American journal of human genetics·2025
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
Same journal

Informative Relational Learning for Adverse Reaction Prediction with Enhanced Generalization to Novel Drugs.

Bioinformatics (Oxford, England)·2026
Same journal

An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.

Bioinformatics (Oxford, England)·2026
Same journal

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

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

Related Experiment Video

Updated: Mar 30, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.6K

BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments.

Brandon R Thomas1, Lily A Chylek2, Joshua Colvin1

  • 1Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA.

Bioinformatics (Oxford, England)
|November 12, 2015
PubMed
Summary
This summary is machine-generated.

BioNetFit is a new tool that optimizes parameter values for rule-based models, improving consistency with simulation data. It leverages distributed computing for computationally expensive model fitting.

More Related Videos

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

1.3K
FIBS-enabled Noninvasive Metabolic Profiling
09:16

FIBS-enabled Noninvasive Metabolic Profiling

Published on: February 3, 2014

10.4K

Related Experiment Videos

Last Updated: Mar 30, 2026

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
08:31

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions

Published on: December 1, 2020

5.6K
Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
05:08

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

1.3K
FIBS-enabled Noninvasive Metabolic Profiling
09:16

FIBS-enabled Noninvasive Metabolic Profiling

Published on: February 3, 2014

10.4K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Rule-based modeling is crucial for analyzing complex biological systems.
  • Specialized simulators like BioNetGen and NFsim are used for rule-based model analysis.
  • Parameter fitting is essential for reconciling model predictions with experimental data.

Purpose of the Study:

  • To introduce BioNetFit, a versatile fitting tool for rule-based models.
  • To enhance the compatibility of fitting procedures with existing simulators (BioNetGen, NFsim).
  • To facilitate computationally intensive parameter optimization using distributed computing.

Main Methods:

  • BioNetFit is a general-purpose fitting tool compatible with BioNetGen and NFsim.
  • The software is designed to utilize distributed computing resources.
  • Parameter optimization is achieved through fitting simulations to data.

Main Results:

  • BioNetFit enables efficient parameter value optimization for rule-based models.
  • The tool supports computationally expensive simulations by employing distributed computing.
  • It ensures consistency between model parameters and experimental data.

Conclusions:

  • BioNetFit provides a robust solution for parameter fitting in rule-based modeling.
  • Its compatibility and distributed computing capabilities make it valuable for systems biology research.
  • The tool enhances the predictive power of computational models by improving parameter accuracy.