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

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

429
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
429
What is Natural Selection?01:32

What is Natural Selection?

129.7K
Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
129.7K
Antibiotic Selection00:57

Antibiotic Selection

60.1K
Overview
60.1K
Node Analysis for AC Circuits01:14

Node Analysis for AC Circuits

682
Consider an angioplasty system featuring a catheter equipped with a turbine, a critical tool for removing plaque deposits from coronary arteries. This intricate medical device operates using a circuit model reminiscent of a dual-node RLC circuit powered by a current-controlled voltage source.
To unravel the complexities of this system, nodal analysis is employed, a powerful technique founded on Kirchhoff's current law (KCL), which remains valid for phasors. AC circuits can effectively be...
682
Types of Selection01:46

Types of Selection

45.3K
Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
45.3K
Frequency-dependent Selection01:21

Frequency-dependent Selection

24.2K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.2K

You might also read

Related Articles

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

Sort by
Same author

Genome-wide absolute quantification of chromatin looping.

Nature structural & molecular biology·2026
Same author

Live-cell imaging of enhancer-promoter dynamics reveals transient contact-driven gene activation.

bioRxiv : the preprint server for biology·2026
Same author

Anisotropic stretch biases the self-organization of actin fibers in multicellular Hydra aggregates.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Genome-wide absolute quantification of chromatin looping.

bioRxiv : the preprint server for biology·2025
Same author

Concentration buffering and noise reduction in non-equilibrium phase-separating systems.

Cell systems·2025
Same author

Correction to "Cell-Free Gene Expression Dynamics in Synthetic Cell Populations".

ACS synthetic biology·2024
Same journal

Quantum simulation of alignment dependent differential cross sections in co-propagating molecular beams at cold collision energies.

The Journal of chemical physics·2026
Same journal

Non-additive ion effects on the coil-globule equilibrium of a generic polymer in aqueous salt solutions.

The Journal of chemical physics·2026
Same journal

Insights into the unexpected small reduction of the temperature of maximum density of water by lithium chloride addition.

The Journal of chemical physics·2026
Same journal

Optical frequency comb double-resonance spectroscopy of the 9030-9175 cm-1 states of ethylene.

The Journal of chemical physics·2026
Same journal

Time reversal breaking of colloidal particles in cells.

The Journal of chemical physics·2026
Same journal

Photodynamics of amino acids under UV excitation: Extraterrestrial amino acids.

The Journal of chemical physics·2026
See all related articles

Related Experiment Video

Updated: Feb 11, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

18.0K

Selected-node stochastic simulation algorithm.

Lorenzo Duso1, Christoph Zechner1

  • 1Center for Systems Biology Dresden and Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.

The Journal of Chemical Physics
|May 3, 2018
PubMed
Summary
This summary is machine-generated.

We developed a new stochastic simulation algorithm (snSSA) that significantly speeds up the analysis of complex biochemical networks by selectively simulating key molecular species. This method offers computational efficiency and improved statistical accuracy for biological system modeling.

More Related Videos

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.5K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K

Related Experiment Videos

Last Updated: Feb 11, 2026

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

18.0K
Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

5.5K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K

Area of Science:

  • Computational Biology
  • Biochemical Systems Analysis
  • Systems Biology

Background:

  • Stochastic simulations are crucial for understanding cellular dynamics.
  • Current simulation methods face significant computational challenges.
  • Efficient simulation of large biochemical networks remains a critical problem.

Purpose of the Study:

  • Introduce a novel algorithm, the selected-node stochastic simulation algorithm (snSSA).
  • Enable selective simulation of molecular species in complex reaction networks.
  • Address computational difficulties in biochemical network simulations.

Main Methods:

  • Analytical elimination of non-selected chemical species.
  • Continuous description of eliminated species using statistical moments.
  • Utilizing a stochastic filtering equation for species description.
  • Comparison with Gillespie's stochastic simulation algorithm (SSA).

Main Results:

  • Achieved substantial speedup compared to Gillespie's SSA.
  • Demonstrated significant variance reduction in statistical results.
  • Lowered the number of Monte Carlo samples required for performance.
  • Reduced simulation time by orders of magnitude in biological case studies.

Conclusions:

  • The snSSA offers a computationally efficient approach for stochastic simulations.
  • The algorithm provides improved statistical accuracy and reduced sampling needs.
  • snSSA is effective for analyzing large and complex biological reaction networks.