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

Population Growth00:57

Population Growth

23.1K
Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.
23.1K
Modeling with Differential Equations01:25

Modeling with Differential Equations

328
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
328
What is Population Genetics?01:25

What is Population Genetics?

53.8K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
53.8K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

53.0K
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).
53.0K
Life Histories01:29

Life Histories

16.3K
Overview
16.3K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
438

You might also read

Related Articles

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

Sort by
Same author

GMP-like and MLP-like Subpopulations of Hematopoietic Stem and Progenitor Cells Harboring Mutated <i>EZH2</i> and <i>TP53</i> at Diagnosis Promote Acute Myeloid Leukemia Relapse: Data of Combined Molecular, Functional, and Genomic Single-Stem-Cell Analyses.

International journal of molecular sciences·2025
Same author

Tracking Somatic Mutations for Lineage Reconstruction.

Methods in molecular biology (Clifton, N.J.)·2025
Same author

United for change: deliberative coalition formation to change the status quo.

Social choice and welfare·2024
Same author

No democracy, no academia.

Science (New York, N.Y.)·2023
Same author

Regulation strategies for two-output biomolecular networks.

Journal of the Royal Society, Interface·2023
Same author

Leveraging Comprehensive Health Records for Breast Cancer Risk Prediction: A Binational Assessment.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2023
Same journal

Covariance decomposition for distance based species tree estimation.

BMC bioinformatics·2026
Same journal

SNPio: a Python interface for population genomic data processing.

BMC bioinformatics·2026
Same journal

SpaHNR: a spatial domain identification method via sparse attention-based hierarchical node representation and multi-view contrastive learning.

BMC bioinformatics·2026
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Apr 26, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K

Lineage grammars: describing, simulating and analyzing population dynamics.

Adam Spiro, Luca Cardelli, Ehud Shapiro1

  • 1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. ehud.shapiro@weizmann.ac.il.

BMC Bioinformatics
|July 23, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces environment-dependent Stochastic Tree Grammars (eSTG) for modeling biological population dynamics. eSTG offers a flexible way to simulate complex systems influenced by environmental factors, not just direct interactions.

More Related Videos

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K

Related Experiment Videos

Last Updated: Apr 26, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.2K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K

Area of Science:

  • Computational Biology
  • Systems Biology
  • Mathematical Biology

Background:

  • Modeling complex biological systems requires precise descriptions of dynamics.
  • Existing formalisms like CRNs and Process Algebras often lack interaction details.
  • Environmental context is crucial but often overlooked in biological modeling.

Purpose of the Study:

  • To present a novel language for describing population dynamics that abstracts individual interactions.
  • To capture the effects of the changing environment on biological systems.
  • To enable simulation and analysis of complex biological phenomena.

Main Methods:

  • Utilized environment-dependent Stochastic Tree Grammars (eSTG).
  • Defined eSTG using context-free stochastic tree grammar transition rules.
  • Allowed transition rule probabilities and rates to depend on global parameters (e.g., population size, time).

Main Results:

  • Demonstrated eSTG's utility in describing population dynamics across multiple biological levels (cellular, tissue, organismal).
  • Showcased eSTG for systems where environmental factors regulate dynamics and species' fate.
  • Generated lineage trees from eSTG execution to analyze evolutionary and developmental histories.

Conclusions:

  • The eSTG formalism facilitates easy specification, simulation, and analysis of complex biological systems.
  • Supports modular and hierarchical definition of biological systems.
  • Effectively models stochastic dynamic behaviors driven by global environmental feedback.