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

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved DNA...

You might also read

Related Articles

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

Sort by
Same author

Porous silicon- and silica-based nanomaterials as smart nano-platforms for development of biosensors: Recent advances, challenges and future aspects.

Biomaterials advances·2026
Same author

Quantitative deciphering of mammalian histamine receptors through mathematical genomics.

Biochemical and biophysical research communications·2026
Same author

The gut-brain axis in arsenic-induced toxicity: mechanisms, consequences, and therapeutic perspectives.

Metabolic brain disease·2026
Same author

Chamomile Modulates Glucose Metabolism and Neuro-inflammation to Alleviate Stress-Induced Depression in Mice.

Molecular neurobiology·2025
Same author

Fast Computational Deep Thermalization.

Physical review letters·2025
Same author

A mathematical genomics perspective on the moonlighting role of glyceraldehyde-3-phosphate dehydrogenase (GAPDH).

International journal of biological macromolecules·2025
Same journal

Predicting piRNA-Disease Associations Based on Dual-View Learning and Multi-head Self-Attention Mechanism Fusion.

Interdisciplinary sciences, computational life sciences·2026
Same journal

DTANet+: Dual Interaction and Kernel-Diverse Network for Drug-Target Affinity Prediction.

Interdisciplinary sciences, computational life sciences·2026
Same journal

STNMAE: Identifying Spatial Domains from Spatial Transcriptomics Data with Neighbor-Aware Multi-view Masked Graph Autoencoder.

Interdisciplinary sciences, computational life sciences·2026
Same journal

Diagnosis and Prediction of Alzheimer's Disease via a High-Level Convolutional Block Attention Module-Residual Network.

Interdisciplinary sciences, computational life sciences·2026
Same journal

Deep3D-DTA: A Tri-Modal Deep Learning Framework for Binding Affinity Prediction Leveraging 3D Structural Representations of Drugs and Targets.

Interdisciplinary sciences, computational life sciences·2026
Same journal

ST-LDAW: A Topic-Model and Damped Weighted Least-Squares Method for Integrative Deconvolution of Single-Cell and Spatial Transcriptomics.

Interdisciplinary sciences, computational life sciences·2026
See all related articles

Related Experiment Video

Updated: May 20, 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

DNA sequence evolution through Integral Value Transformations.

Sk Sarif Hassan1, Pabitra Pal Choudhury, Ranita Guha

  • 1Applied Statistics Unit, Indian Statistical Institute, Kolkata, 700108, India. sarimif@gmail.com

Interdisciplinary Sciences, Computational Life Sciences
|July 31, 2012
PubMed
Summary
This summary is machine-generated.

Cellular Automata (CA) models DNA evolution, but generated sequences lack database matches. New Integral Value Transformations (IVT) map CA-evolved DNA to existing sequences using fractal parameters.

More Related Videos

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites
09:31

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Published on: March 22, 2016

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

Related Experiment Videos

Last Updated: May 20, 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

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites
09:31

Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

Published on: March 22, 2016

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing
11:36

A Protocol for Functional Assessment of Whole-Protein Saturation Mutagenesis Libraries Utilizing High-Throughput Sequencing

Published on: July 3, 2016

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Cellular Automata (CA) is a computational model used to study DNA structure, evolution, and function.
  • Previous research utilized one-dimensional CA with four linear rules to simulate DNA evolution, but generated sequences did not match existing genomic databases.
  • This discrepancy motivated a re-evaluation of DNA evolution modeling from a more fundamental perspective.

Purpose of the Study:

  • To develop a novel computational method for modeling DNA evolution that generates biologically relevant sequences.
  • To establish a transformational mechanism between different DNA sequences using mathematical parameters.
  • To bridge the gap between theoretical CA-based DNA simulations and empirically observed genomic data.

Main Methods:

  • Devised Integral Value Transformations (IVT), an enriched set of discrete transformations, as an alternative to CA for DNA sequence evolution.
  • Applied IVT systematically to simulate DNA sequence transformations over discrete time instances.
  • Incorporated quantitative mathematical parameters, including fractal dimensions, to characterize DNA sequences within the IVT framework.

Main Results:

  • Demonstrated that DNA sequences generated through IVT evolutions can be directly mapped to specific DNA sequences found in established genomic databases.
  • Established a novel transformational mechanism enabling the conversion of one DNA sequence to another via IVT.
  • Successfully linked theoretical DNA modeling with real-world genomic data through a quantitative, fractal-based approach.

Conclusions:

  • Integral Value Transformations (IVT) offer a more effective paradigm than traditional Cellular Automata (CA) for modeling DNA evolution.
  • The developed IVT method, incorporating fractal mathematics, provides a robust mechanism for generating and mapping biologically relevant DNA sequences.
  • This research establishes a quantitative link between computational models and genomic databases, advancing our understanding of DNA dynamics.