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

Quadratic Models01:23

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
Morphogenesis02:19

Morphogenesis

Plant morphogenesis—the development of a plant’s form and structure—involves several overlapping developmental processes, including growth and cell differentiation. Precursor cells differentiate into specific cell types, which are organized into the tissues and organ systems that make up the functional plant.
Evolution of New Traits in Microbes01:24

Evolution of New Traits in Microbes

Microorganisms evolve rapidly due to their large population sizes and short generation times, often exhibiting measurable changes within days under laboratory conditions. Natural selection acts on standing genetic variation, enabling the retention and amplification of beneficial traits that confer fitness advantages in changing environments.Adaptive Pigment Regulation in RhodobacterIn Rhodobacter, a genus of purple non-sulfur bacteria, light-harvesting pigments such as bacteriochlorophyll and...
Geometry of Hyperbolas01:30

Geometry of Hyperbolas

A hyperbola consists of all points where the absolute difference of distances to two fixed points, called foci, remains constant. The standard equation isEach branch extends infinitely and approaches two asymptotes, which guide the curve’s behavior. The parameters a and b define key features: a measures the distance from the center to each vertex along the transverse axis, while b influences the slopes of the asymptotes. The asymptotes have equationsA rectangle centered at the origin with...
Modeling with Differential Equations01:25

Modeling with Differential Equations

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...
Evolutionary Psychology01:20

Evolutionary Psychology

Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the human psyche...

You might also read

Related Articles

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

Sort by
Same author

Oscillatory co-expression of HES1 and HES5 enables a hybrid state in a cross-repressive transcription factor regulatory motif.

Development (Cambridge, England)·2026
Same author

Foci, waves, excitability: Self-organization of phase waves in a model of asymmetrically coupled embryonic oscillators.

Physical review. E·2026
Same author

Learning the Principles of T Cell Antigen Discernment.

Annual review of biophysics·2026
Same author

The hard truth about how hard it is to publish in Development.

Development (Cambridge, England)·2026
Same author

Generative epigenetic landscapes map the topology and topography of cell fates.

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

Reconstructing Waddington's landscape from data.

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

Related Experiment Video

Updated: May 18, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Phenotypic models of evolution and development: geometry as destiny.

Paul François1, Eric D Siggia

  • 1McGill University, 3600 rue University, H3A2T8, Montreal, QC, Canada. paulf@physics.mcgill.ca

Current Opinion in Genetics & Development
|October 3, 2012
PubMed
Summary

Computational evolution simplifies developmental models, reducing complex gene interactions to a geometric cell state map. This approach explains somitogenesis patterns like Hox gene expression and posterior dominance without needing detailed genetic regulation data.

More Related Videos

Probing the Roles of Physical Forces in Early Chick Embryonic Morphogenesis
06:33

Probing the Roles of Physical Forces in Early Chick Embryonic Morphogenesis

Published on: June 5, 2018

Experimental Manipulation of Body Size to Estimate Morphological Scaling Relationships in Drosophila
06:00

Experimental Manipulation of Body Size to Estimate Morphological Scaling Relationships in Drosophila

Published on: October 1, 2011

Related Experiment Videos

Last Updated: May 18, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Probing the Roles of Physical Forces in Early Chick Embryonic Morphogenesis
06:33

Probing the Roles of Physical Forces in Early Chick Embryonic Morphogenesis

Published on: June 5, 2018

Experimental Manipulation of Body Size to Estimate Morphological Scaling Relationships in Drosophila
06:00

Experimental Manipulation of Body Size to Estimate Morphological Scaling Relationships in Drosophila

Published on: October 1, 2011

Area of Science:

  • Developmental biology
  • Computational modeling
  • Evolutionary computation

Background:

  • Quantitative developmental models often face challenges with extensive parameters and fitting embryonic data.
  • Computational evolution offers a method to create simpler phenotype models with fewer variables.

Purpose of the Study:

  • To demonstrate how computational evolution can simplify developmental models.
  • To represent the clock and wavefront model of somitogenesis using discrete dynamical transitions.
  • To show that key phenotypes like Hox gene expression and posterior dominance can emerge from this model naturally.

Main Methods:

  • Utilizing computational evolution to generate phenotype models with reduced parameters.
  • Representing the clock and wavefront model as a sequence of two discrete dynamical transitions (bifurcations).
  • Analyzing the emergence of expression-time to space maps for Hox genes and the posterior dominance rule.

Main Results:

  • Computational evolution successfully produced simplified models of phenotype.
  • The clock and wavefront model of somitogenesis was effectively represented by two bifurcations.
  • Key developmental phenotypes, including Hox gene expression patterns and posterior dominance, were explained without explicit genetic regulation details.

Conclusions:

  • Computational evolution provides a powerful framework for understanding developmental patterning with fewer parameters.
  • The geometrical representation of cell state movement simplifies complex developmental dynamics.
  • This approach offers insights into evolutionary mechanisms underlying developmental phenotypes.