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

Wave Parameters01:10

Wave Parameters

9.3K
The simplest mechanical waves are associated with simple harmonic motion and repeat themselves for several cycles. These simple harmonic waves can be modeled using a combination of sine and cosine functions. Consider a simplified surface water wave that moves across the water's surface. Unlike complex ocean waves, in surface water waves, water moves vertically, oscillating up and down, whereas the disturbance of the wave moves horizontally through the medium. If a seagull is floating on the...
9.3K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

246
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...
246
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.1K
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...
5.1K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

312
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
312
Noncompartmental Analysis: Miscellaneous Pharmacokinetic Parameters00:54

Noncompartmental Analysis: Miscellaneous Pharmacokinetic Parameters

446
The noncompartmental approach is a widely used method in pharmacokinetics to assess drugs' behaviors in the body. It considers several factors, including clearance, bioavailability, and total volume of distribution.
One key aspect of the noncompartmental approach is determining a drug's total clearance. This can be done by dividing the drug dose by the area under the concentration-time curve from zero to infinity. The area under the concentration-time curve represents the drug's...
446
Dosage Regimens: Partial Pharmacokinetic Parameters01:01

Dosage Regimens: Partial Pharmacokinetic Parameters

165
It is not uncommon for complete drug pharmacokinetic profiles to remain elusive in pharmacokinetics. This necessitates certain educated assumptions by pharmacokineticists to determine appropriate dosage regimens without comprehensive pharmacokinetic data from animal or human studies. One prevalent assumption is setting the bioavailability factor, denoted as F, to 1 or 100%. This assumption caters to the scenario where a drug doesn't achieve full systemic absorption, resulting in the patient...
165

You might also read

Related Articles

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

Sort by
Same author

Model-based inference of enzyme inhibitions from perturbation-induced metabolic dynamics.

bioRxiv : the preprint server for biology·2026
Same author

A compact model of Escherichia coli core and biosynthetic metabolism.

PLoS computational biology·2025
Same author

Complexity reduction by symmetry: Uncovering the minimal regulatory network for logical computation in bacteria.

PLoS computational biology·2025
Same author

Optimal enzyme profiles in unbranched metabolic pathways.

Interface focus·2024
Same author

Complexity reduction by symmetry: uncovering the minimal regulatory network for logical computation in bacteria.

ArXiv·2023
Same author

A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models.

Nature communications·2023
Same journal

Cross-Domain Transfer Learning from Peptides to Metabolites Using a Multi-Property Fine-Tuned LLM.

Bioinformatics (Oxford, England)·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

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

Related Experiment Video

Updated: Jan 28, 2026

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes
11:44

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes

Published on: November 12, 2016

18.6K

Parameter balancing: consistent parameter sets for kinetic metabolic models.

Timo Lubitz1, Wolfram Liebermeister2,3

  • 1Theoretische Biophysik, Institut für Biologie, Humboldt-Universität zu Berlin, Berlin, Germany.

Bioinformatics (Oxford, England)
|February 23, 2019
PubMed
Summary
This summary is machine-generated.

Parameter balancing refines uncertain kinetic data for metabolic models, providing complete and consistent parameter sets. This method also quantifies uncertainty ranges for all model parameters, enhancing model reliability.

More Related Videos

Intravitreal Injection and Quantitation of Infection Parameters in a Mouse Model of Bacterial Endophthalmitis
07:24

Intravitreal Injection and Quantitation of Infection Parameters in a Mouse Model of Bacterial Endophthalmitis

Published on: February 6, 2021

12.9K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.0K

Related Experiment Videos

Last Updated: Jan 28, 2026

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes
11:44

Spin Saturation Transfer Difference NMR SSTD NMR: A New Tool to Obtain Kinetic Parameters of Chemical Exchange Processes

Published on: November 12, 2016

18.6K
Intravitreal Injection and Quantitation of Infection Parameters in a Mouse Model of Bacterial Endophthalmitis
07:24

Intravitreal Injection and Quantitation of Infection Parameters in a Mouse Model of Bacterial Endophthalmitis

Published on: February 6, 2021

12.9K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.0K

Area of Science:

  • Systems Biology
  • Biochemical Engineering

Background:

  • Kinetic constants are crucial for metabolic models but often suffer from uncertainty, inconsistency, and incompleteness.
  • Accurate kinetic data is essential for reliable metabolic modeling and understanding cellular processes.

Purpose of the Study:

  • To introduce parameter balancing as a method for generating complete and consistent kinetic parameter sets for metabolic models.
  • To provide tools that account for predefined ranges and physical constraints in biochemical data.
  • To enable the integration of kinetic, thermodynamic, and metabolomic data for robust metabolic state prediction.

Main Methods:

  • Parameter balancing employs Bayesian regression to estimate the most plausible parameter set.
  • The method incorporates predefined ranges and physical constraints for biochemical constants.
  • It allows customization of prior distributions and constraints for kinetic and thermodynamic data.

Main Results:

  • Parameter balancing yields complete and consistent parameter sets from uncertain input data.
  • The approach provides uncertainty ranges for all model parameters, crucial for sensitivity analysis.
  • It enables the derivation of thermodynamically consistent metabolic states aligned with user-defined flux directions.

Conclusions:

  • Parameter balancing offers a robust solution for handling data limitations in metabolic modeling.
  • The developed tools facilitate the integration of diverse biochemical data for improved model accuracy.
  • This approach enhances the reliability of metabolic models for systems biology research.