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

323
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...
323
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

562
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
562
Other Glycolytic Pathways01:24

Other Glycolytic Pathways

1.2K
The pentose phosphate pathway (PPP) operates in parallel with glycolysis, facilitating the metabolism of both pentoses and glucose. This pathway consists of two distinct phases: the oxidative and non-oxidative phases. While it does not directly generate ATP, the intermediates formed during the process can integrate into glycolysis, contributing to cellular energy metabolism when required.Oxidative Phase: NADPH ProductionThe oxidative phase of the pentose phosphate pathway is primarily...
1.2K
Respiration Pathways01:26

Respiration Pathways

937
Cellular respiration is a fundamental metabolic process that enables organisms to generate energy from organic molecules. One of its central pathways is the tricarboxylic acid (TCA) cycle, also known as the Krebs cycle, which plays a crucial role in energy production and biosynthetic processes.Conversion of Pyruvate to Acetyl-CoAThe pyruvate generated from glycolysis undergoes oxidative decarboxylation by the pyruvate dehydrogenase complex, producing acetyl-CoA, one molecule of NADH, and one...
937
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

363
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...
363
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

416
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...
416

You might also read

Related Articles

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

Sort by
Same author

Different genomic footprint of small insertion-deletion and structural variants determines the genetic divergence of indica and japonica rice.

BMC genomics·2026
Same author

A unified multimodal model for generalizable zero-shot and supervised protein function prediction.

Bioinformatics (Oxford, England)·2026
Same author

CryoFSL: an annotation-efficient, few-shot learning framework for robust protein particle picking in cryo-electron microscopy micrographs.

Briefings in bioinformatics·2026
Same author

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Integrating protein and DNA embeddings for improving genome-wide transcription factor binding site prediction.

NAR genomics and bioinformatics·2026
Same author

Ultrafine Nanoporous Ordered High-Entropy Intermetallics with Isolated Multisites toward Electrocatalytic Aldehyde Hydrogenation.

Journal of the American Chemical Society·2026

Related Experiment Video

Updated: Apr 21, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.3K

Reconstruction of metabolic pathways by combining probabilistic graphical model-based and knowledge-based methods.

Qi Qi1, Jilong Li1, Jianlin Cheng2

  • 1Department of Computer Science, University of Missouri, Columbia, MO 65201, USA.

BMC Proceedings
|November 7, 2014
PubMed
Summary

This study introduces a novel bioinformatics approach combining existing pathway knowledge with a Bayesian model to improve automatic metabolic network reconstruction. The method enhances both accuracy and coverage compared to traditional and ab initio techniques.

More Related Videos

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

18.7K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.9K

Related Experiment Videos

Last Updated: Apr 21, 2026

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

2.3K
A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

18.7K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.9K

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Automatic reconstruction of metabolic pathways from genomic and transcriptomic data is crucial but challenging.
  • Traditional methods (knowledge-based mapping) often yield incomplete pathways and miss novel reactions.
  • Ab initio methods predict novel interactions but suffer from low accuracy and high false positives.

Purpose of the Study:

  • To develop an improved method for automatic metabolic network reconstruction.
  • To enhance both the accuracy and coverage of predicted metabolic pathways.
  • To integrate existing pathway knowledge with a novel ab initio approach.

Main Methods:

  • Constructed a knowledge database of known gene/protein interactions and metabolic reactions.
  • Utilized a Bayesian probabilistic graphical model for network learning.
  • Applied known reactions and interactions as constraints for pathway prediction.
  • Integrated knowledge-based and ab initio strategies.

Main Results:

  • The combined approach improved metabolic network reconstruction coverage compared to reference pathway mapping.
  • The method demonstrated higher accuracy than pure ab initio approaches.
  • Successfully constructed metabolic networks from yeast gene expression data.
  • Validated results against 62 known metabolic networks in the KEGG database.

Conclusions:

  • The probabilistic knowledge-based approach offers a significant advancement in automatic metabolic network construction.
  • Integrating prior biological knowledge with probabilistic models enhances prediction reliability.
  • This method provides a more comprehensive and accurate tool for understanding organism-specific metabolism.