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

Plasticity00:58

Plasticity

2.5K
Plasticity is the property where an object loses its elasticity and undergoes irreversible deformation, even after the deformation forces are eliminated. If a material deforms irreversibly without increasing stress or load, then this is called ideal plasticity. For example, when a force is applied to an aluminum rod, it changes its shape, but it does not return to its original shape once the force is removed. Plastic deformation or ductility is thus a permanent deformation or change in the...
2.5K
Neuroplasticity01:01

Neuroplasticity

663
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
663
Plastic Deformations01:19

Plastic Deformations

167
Plastic deformation represents a fundamental concept in materials science, which explains the irreversible change in the shape of a material when it experiences stress beyond its elastic capability. This phenomenon is important in structural engineering, especially in designing and analyzing cantilever beams—structures that are securely fixed at one end and bear loads at the opposite end. When these beams are subjected to loads within their elastic range, they will return to their...
167
Plastic Behavior01:21

Plastic Behavior

245
A material's elastic behavior is characterized by the disappearance of stress once the load is removed, allowing the material to return to its original state. However, when stress surpasses the yield point, yielding commences, marking the onset of plastic deformation or permanent set. This change from elastic to plastic behavior is influenced by the peak stress value and the duration before the load is removed. An intriguing observation occurs when a specimen is loaded, unloaded, and...
245
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

74
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
74
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

93
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...
93

You might also read

Related Articles

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

Sort by
Same author

Mechanism of evolution by genetic assimilation : Equivalence and independence of genetic mutation and epigenetic modulation in phenotypic expression.

Biophysical reviews·2018
Same journal

Navigating the labyrinth of drugging the disordered.

Biophysical reviews·2026
Same journal

<i>Biophysical Reviews</i>: a forum for publication of review articles from the international biophysics community.

Biophysical reviews·2026
Same journal

Mitochondrial potassium channels: mitochondria-specific mechanism of regulation.

Biophysical reviews·2026
Same journal

Biomolecular condensates in living systems: from function to disease. What to do next.

Biophysical reviews·2026
Same journal

Astrocyte morphology: complex or trivial?

Biophysical reviews·2026
Same journal

Correction to: A quest for greater thermodynamic rigour in the quantitative characterization of protein self-association by direct assessment of sedimentation equilibrium distributions.

Biophysical reviews·2026
See all related articles

Related Experiment Video

Updated: Aug 13, 2025

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

6.9K

Computational modelling of plasticity-led evolution.

Eden Tian Hwa Ng1, Akira R Kinjo1

  • 1Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410 Brunei Darussalam.

Biophysical Reviews
|January 20, 2023
PubMed
Summary
This summary is machine-generated.

Plasticity-led evolution offers a new framework for understanding how organisms adapt to environmental changes. Computational models of gene regulatory networks are crucial for exploring its mechanisms and predictions.

Keywords:
Adaptive plastic responseArtificial recurrent neural networksEvo-devoGene regulatory networksGenetic accommodationPhenotypic plasticity

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.8K
Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

15.5K

Related Experiment Videos

Last Updated: Aug 13, 2025

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

6.9K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.8K
Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
11:56

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity

Published on: November 11, 2017

15.5K

Area of Science:

  • Evolutionary biology
  • Developmental biology
  • Computational biology

Background:

  • The Modern Evolutionary Synthesis struggles to explain rapid adaptation to large environmental changes.
  • Phenotypic plasticity offers a potential mechanism for rapid evolutionary responses.
  • Plasticity-led evolution proposes that environmentally induced traits can become genetically assimilated.

Purpose of the Study:

  • To review computational modeling approaches for plasticity-led evolution.
  • To explore the role of gene regulatory networks in this evolutionary process.
  • To provide insights into the mechanisms and predictions of plasticity-led evolution.

Main Methods:

  • Review of computational models, specifically gene regulatory network (GRN) models.
  • Analysis of GRN models incorporating development, gene-environment interactions, genetics, and natural selection.
  • Comparison of GRNs with artificial recurrent neural networks (ANNs).

Main Results:

  • GRN models can integrate multiple factors crucial for plasticity-led evolution.
  • Computational modeling consolidates the criteria for plasticity-led evolution.
  • Analogies and discrepancies between GRNs and ANNs offer further mechanistic insights.

Conclusions:

  • Computational modeling, particularly with GRNs, is essential for understanding plasticity-led evolution.
  • GRNs provide a framework to study the interplay of plasticity, development, and genetics.
  • Further research into GRN-ANN relationships can deepen our understanding of evolutionary mechanisms.