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

The Evidence for Evolution02:55

The Evidence for Evolution

47.9K
Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
47.9K
Convergent Evolution01:54

Convergent Evolution

32.7K
Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
32.7K
Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

9.1K
While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
9.1K
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

996
In structural engineering, the equilibrium of a system is not only determined by its equations of equilibrium but also with the help of constraints. Constraints refer to restrictions on the motion of a system. The proper combinations of constraints can minimize the total number of constraints needed to maintain a system in mechanical equilibrium. When this happens, the system is said to be statically determinate. For such systems, the unknown reaction supports can be estimated using equilibrium...
996
Eukaryotic Evolution01:24

Eukaryotic Evolution

40.7K
The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the nucleus-first hypothesis, the ancestral prokaryote first evolved a membrane to enclose DNA and form the nucleus. Conversely, the mitochondria-first hypothesis suggests that the nucleus was formed after endosymbiosis of mitochondria.
Contrary to the endosymbiont theory, the eukaryote-first hypothesis proposes that the simpler prokaryotic and...
40.7K
Synteny and Evolution02:31

Synteny and Evolution

3.8K
John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral...
3.8K

You might also read

Related Articles

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

Sort by
Same author

Can a Good Theory Be Built Using Bad Ingredients?

Computational brain & behavior·2026
Same author

Validation of polymorphic Gompertzian model of cancer through in vitro and in vivo data.

PloS one·2025
Same author

Measuring competitive exclusion in non-small cell lung cancer.

Science advances·2022
Same author

The Contribution of Evolutionary Game Theory to Understanding and Treating Cancer.

Dynamic games and applications·2022
Same author

Fractionated follow-up chemotherapy delays the onset of resistance in bone metastatic prostate cancer.

Games·2021
Same author

IsoMaTrix: a framework to visualize the isoclines of matrix games and quantify uncertainty in structured populations.

Bioinformatics (Oxford, England)·2020

Related Experiment Video

Updated: Jan 28, 2026

Molecular Evolution of the Tre Recombinase
12:02

Molecular Evolution of the Tre Recombinase

Published on: May 29, 2008

10.1K

Computational Complexity as an Ultimate Constraint on Evolution.

Artem Kaznatcheev1,2

  • 1Department of Computer Science, University of Oxford, Oxford, OX1 3QD, United Kingdom kaznatcheev.artem@gmail.com.

Genetics
|March 6, 2019
PubMed
Summary

Evolutionary fitness landscapes can be computationally hard, preventing quick evolution to local optima. This complexity, termed "hard landscapes," may explain open-ended evolution and unbounded fitness growth in nature.

Keywords:
computational complexityevolutionary constraintsfitness landscapesopen-ended evolutionpower law

More Related Videos

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.9K
Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
09:01

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Published on: March 16, 2011

31.1K

Related Experiment Videos

Last Updated: Jan 28, 2026

Molecular Evolution of the Tre Recombinase
12:02

Molecular Evolution of the Tre Recombinase

Published on: May 29, 2008

10.1K
A Method for Tracking the Time Evolution of Steady-State Evoked Potentials
12:03

A Method for Tracking the Time Evolution of Steady-State Evoked Potentials

Published on: May 25, 2019

8.9K
Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
09:01

Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli

Published on: March 16, 2011

31.1K

Area of Science:

  • Evolutionary biology
  • Theoretical computer science
  • Computational complexity

Background:

  • Evolutionary fitness landscapes often exhibit epistasis, a combinatorial structure.
  • Traditional models assume local fitness optima are easily reachable.
  • This assumption is challenged by computational constraints in certain landscapes.

Purpose of the Study:

  • Introduce a distinction between "easy" and "hard" fitness landscapes.
  • Characterize the computational complexity of evolutionary processes.
  • Explore implications for understanding natural evolution and open-endedness.

Main Methods:

  • Theoretical analysis of fitness landscapes.
  • Distinguishing landscape types based on epistasis and computational difficulty.
  • Applying concepts from theoretical computer science and combinatorial optimization.

Main Results:

  • Defined "easy" landscapes (traditional) and "hard" landscapes (computationally constrained).
  • Demonstrated "hard" landscapes exist even without reciprocal sign epistasis (semismooth).
  • Showed hard landscapes necessitate power-law fitness advantage, enabling open-ended evolution.

Conclusions:

  • Computational complexity is a fundamental constraint on evolutionary trajectories.
  • Hard landscapes provide a framework for understanding open-ended evolution and unbounded fitness growth.
  • Further empirical research is needed to determine the prevalence of hard landscapes in nature.