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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

124
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...
124
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

107
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
107
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

877
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
877
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

91
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
91
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

147
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
147
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

124
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
124

You might also read

Related Articles

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

Sort by
Same author

Large language models instantiate evolutionarily robust strategies of cooperation.

PNAS nexus·2026
Same author

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

Frontiers in immunology·2026
Same author

Structural hormesis in protein aggregation: A minimal mechanistic model.

Journal of the Royal Society, Interface·2026
Same author

Data-driven Mori-Zwanzig modeling of Lagrangian particle dynamics in turbulent flows.

Proceedings of the National Academy of Sciences of the United States of America·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

Embryonic stem cell-derived extracellular vesicles delay cellular senescence by inhibiting oxidative stress.

The Journal of biological chemistry·2025
Same journal

Poisoning the Genome: Targeted Backdoor Attacks on DNA Foundation Models.

ArXiv·2026
Same journal

Mechanistic mathematical model of the in vitro infection dynamics of Bunyamwera and Batai viruses including MOI-dependent shortening of the eclipse phase.

ArXiv·2026
Same journal

AI-Driven Lumped-Element Modeling of Human Respiratory System for Studying Voice Mechanics.

ArXiv·2026
Same journal

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI.

ArXiv·2026
Same journal

Feynman Kac Reweighted Schrödinger Bridge Matching for Surface-Based Tau PET Harmonization.

ArXiv·2026
Same journal

Agentic Discovery of Non-Canonical Antimicrobial Peptides with AMPGAN v3.

ArXiv·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 2025

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K

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

Ely F Miller1, Abhishek Mallela2,3, Jacob Neumann4

  • 1Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, United States of America.

Arxiv
|September 5, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for parameterizing mathematical models of cellular systems using qualitative data. PyBioNetFit software enables reproducible analysis and uncertainty quantification for systems biology models.

Keywords:
Bayesian inferenceCurve-fittingMarkov chain Monte Carlo (MCMC)Maximum likelihood estimation (MLE)Profile likelihood

More Related Videos

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.9K
High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
08:48

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water

Published on: April 28, 2022

1.8K

Related Experiment Videos

Last Updated: Sep 9, 2025

A Quantitative Fitness Analysis Workflow
11:39

A Quantitative Fitness Analysis Workflow

Published on: August 13, 2012

14.6K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.9K
High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water
08:48

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water

Published on: April 28, 2022

1.8K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Cellular Signaling

Background:

  • Cellular regulatory system studies often yield qualitative data, like rank-ordered responses, which are difficult to integrate into mathematical models.
  • Previous methods for incorporating qualitative data into ordinary differential equation (ODE) models were often ad hoc, non-reproducible, and lacked uncertainty quantification.

Purpose of the Study:

  • To develop a systematic and automated approach for parameterizing ODE models of cellular regulatory systems using both qualitative and quantitative data.
  • To improve the reusability of qualitative biological observations in mathematical modeling.
  • To implement uncertainty quantification (UQ) in systems biology model parameterization.

Main Methods:

  • Formalized qualitative observations from biological data.
  • Utilized the PyBioNetFit software package for automated model parameterization.
  • Integrated qualitative and quantitative data within an ODE model framework.
  • Performed uncertainty quantification (UQ).

Main Results:

  • PyBioNetFit successfully leveraged qualitative data alongside quantitative data for model parameterization.
  • The automated approach improved reproducibility and enabled uncertainty quantification, which was absent in prior methods.
  • Demonstrated a more reliable estimation of model parameters for systems biology.

Conclusions:

  • PyBioNetFit provides a robust framework for integrating qualitative and quantitative data in systems biology modeling.
  • The developed method enhances the reliability of parameter estimation and facilitates crucial uncertainty quantification.
  • This approach is vital for reproducible and insightful analyses of cellular regulatory systems.