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

Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

65.5K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
65.5K
Speciation Rates01:07

Speciation Rates

23.3K
Overview
23.3K
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

84
In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
84

You might also read

Related Articles

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

Sort by
Same author

Anatomy and Spread of Gingivobuccal Cancers.

Oral and maxillofacial surgery clinics of North America·2026
Same author

Percutaneous Minimal Access Approach for Open Reduction and Internal Fixation of Zygomatic Arch in Zygomatic Complex Fractures: A Simple and Effective Technique.

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons·2026
Same author

Global Trends in Postoperative Sepsis After Pancreatoduodenectomy: A Bibliometric Analysis.

The Journal of surgical research·2026
Same author

Regulated cell death in sepsis-associated liver injury: molecular mechanisms and therapeutic implications.

Frontiers in immunology·2026
Same author

Point-of-care Ultrasound (POCUS)-Guided Pragmatic Fluid and Albumin Resuscitation and Hemodynamic Monitoring in Cirrhosis and Septic Shock.

Journal of clinical and experimental hepatology·2026
Same author

Femoropopliteal Artery Atherectomy for Symptomatic Peripheral Artery Disease in the XLPAD Registry.

The American journal of cardiology·2026

Related Experiment Video

Updated: Mar 10, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K

Stochastic Simulation Service: Bridging the Gap between the Computational Expert and the Biologist.

Brian Drawert1, Andreas Hellander2, Ben Bales3

  • 1Department of Computer Science, University of California, Santa Barbara, Santa Barbara, California, United States of America.

Plos Computational Biology
|December 9, 2016
PubMed
Summary

StochSS (Stochastic Simulation as a Service) offers an integrated environment for biochemical system modeling and simulation. This scalable platform enables researchers to easily develop, simulate, and share complex biological models using cloud computing resources.

More Related Videos

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

9.7K
A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments
12:21

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Published on: August 6, 2013

11.1K

Related Experiment Videos

Last Updated: Mar 10, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.6K
Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms
10:41

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

Published on: May 9, 2017

9.7K
A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments
12:21

A Versatile Automated Platform for Micro-scale Cell Stimulation Experiments

Published on: August 6, 2013

11.1K

Area of Science:

  • Biochemistry
  • Computational Biology
  • Systems Biology

Background:

  • Developing accurate computational models for biochemical systems is crucial for understanding cellular processes.
  • Existing tools often lack the flexibility to handle both deterministic and stochastic models or scale computational resources effectively.

Purpose of the Study:

  • To introduce StochSS (Stochastic Simulation as a Service), an integrated development environment for modeling and simulating biochemical systems.
  • To provide researchers with an accessible platform for developing complex biological models with scalable cloud computing capabilities.

Main Methods:

  • StochSS utilizes a user-friendly graphical interface for model development and simulation.
  • It incorporates state-of-the-art simulation engines for both deterministic and discrete stochastic biochemical systems.
  • The platform supports up to three-dimensional simulations and can scale computational resources in the cloud.

Main Results:

  • StochSS enables rapid development and simulation of biological models with increasing complexity.
  • The system seamlessly scales computing resources in the cloud to meet demand.
  • It facilitates multi-user collaboration through shared resources and a public model repository.

Conclusions:

  • StochSS provides an easy-to-use, scalable, and collaborative environment for biochemical system modeling and simulation.
  • The platform empowers researchers to explore complex biological models efficiently.
  • Its cloud-based architecture ensures accessibility and adaptability for diverse research needs.