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

Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
Synthesis and Decomposition Reactions02:17

Synthesis and Decomposition Reactions

Synthesis and decomposition are two types of redox reactions. Synthesis means to make something, whereas decomposition means to break something. The reactions are accompanied by chemical and energy changes.
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
Radical Reactivity: Overview01:11

Radical Reactivity: Overview

Radicals, the highly reactive species, gain stability by undergoing three different reactions. The first reaction involves a radical-radical coupling, in which a radical combines with another radical, forming a spin‐paired molecule. The second reaction is between a radical and a spin‐paired molecule, generating a new radical and a new spin‐paired molecule. The third reaction is radical decomposition in a unimolecular reaction, forming a new radical and a spin‐paired molecule. These three...

You might also read

Related Articles

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

Sort by
Same author

Transcriptional regulatory networks of the human gut symbiont Bacteroides thetaiotaomicron are uncovered using machine learning.

Nucleic acids research·2025
Same author

PMkbase (version 1.0): an interactive web-based tool for tracking bacterial metabolic traits using phenotype microarrays made interoperable with sequence information and visualizing/processing PM data.

Microbiology spectrum·2025
Same author

Regulatory orchestration of FK506 biosynthesis in <i>Streptomyces tsukubaensis</i> NRRL 18488 revealed through systematic analysis.

iScience·2025
Same author

CRISPRi screening reveals <i>E. coli</i>'s anaerobic-like respiratory adaptations to gentamicin: membrane depolarization by CpxR.

mSystems·2025
Same author

Data sharing restrictions are hampering precision health in the European Union.

Nature medicine·2025
Same author

Establishing comprehensive quaternary structural proteomes from genome sequence.

bioRxiv : the preprint server for biology·2024
Same journal

Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice.

BMC systems biology·2019
Same journal

Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.

BMC systems biology·2019
Same journal

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.

BMC systems biology·2019
Same journal

A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

BMC systems biology·2019
Same journal

GNE: a deep learning framework for gene network inference by aggregating biological information.

BMC systems biology·2019
Same journal

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.

BMC systems biology·2019
See all related articles

Related Experiment Video

Updated: Jun 25, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Decomposing complex reaction networks using random sampling, principal component analysis and basis rotation.

Christian L Barrett1, Markus J Herrgard, Bernhard Palsson

  • 1Department of Bioengineering, University of California at San Diego, La Jolla, CA, 92093-0412, USA. cbarrett@bioeng.ucsd.edu

BMC Systems Biology
|March 10, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to analyze cellular metabolic regulation. Our approach simplifies complex metabolic networks, revealing that controlling a few key reactions can govern the entire system's metabolic state.

More Related Videos

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

Related Experiment Videos

Last Updated: Jun 25, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach
09:32

Resolving Water, Proteins, and Lipids from In Vivo Confocal Raman Spectra of Stratum Corneum through a Chemometric Approach

Published on: September 26, 2019

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures
09:38

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Published on: January 7, 2019

Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Biochemical Network Analysis

Background:

  • Cellular metabolism involves extensive molecular activity and complex regulatory mechanisms.
  • Post-translational regulation is a major control mechanism in cellular metabolism.
  • Assessing metabolic regulation in large biochemical networks remains a challenge.

Purpose of the Study:

  • To develop an objective, top-down method for assessing metabolic regulation in large biochemical networks.
  • To simplify the understanding of complex metabolic regulatory problems.

Main Methods:

  • Utilized Monte Carlo sampling of steady-state flux space in cell-scale metabolic systems.
  • Applied Principal Component Analysis (PCA) and eigenvector rotation for data decomposition.
  • Analyzed low-dimensional representations of steady flux states.

Main Results:

  • Achieved a low-dimensional, biochemically interpretable decomposition of metabolic flux states.
  • Identified small sets of reactions responsible for nearly all system flux variability.
  • Demonstrated that regulating a few reaction sets can determine the overall metabolic flux state.

Conclusions:

  • The developed top-down analysis method identifies key regulatory requirements independent of specific parameters.
  • This approach complements reductionist methods for studying regulatory mechanisms.
  • Facilitates a better understanding of global regulatory strategies in biological networks.