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Related Concept Videos

The Evidence for Evolution

The Evidence for Evolution

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.

Convergent Evolution

Convergent Evolution

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.

Eukaryotic Evolution

Eukaryotic Evolution

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...

Synteny and Evolution

Synteny and Evolution

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...

Genome Size and the Evolution of New Genes

Genome Size and the Evolution of New Genes

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.

Resistivity

Resistivity

When a voltage is applied to a conductor, an electrical field is generated, and charges in the conductor feel the force due to the electrical field. The current density that results depends on the electrical field and the properties of the material. In some materials, including metals at a given temperature, the current density is approximately proportional to the electrical field. In these cases, the current density can be modeled as:

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Related Experiment Video

Updated: Feb 9, 2026

Testing the Role of Multicopy Plasmids in the Evolution of Antibiotic Resistance

Testing the Role of Multicopy Plasmids in the Evolution of Antibiotic Resistance

Published on: May 2, 2018

Harnessing Tumor Evolution to Circumvent Resistance.

Katherine L Pogrebniak¹, Christina Curtis²

¹Cancer Biology Program, Stanford University School of Medicine, Stanford, CA 94305, USA; http://med.stanford.edu/curtislab.html.

Trends in Genetics : TIG

|June 16, 2018

Summary

High-throughput sequencing reveals tumor evolution and resistance mechanisms. Understanding these dynamics informs personalized cancer therapies by analyzing genomic and phenotypic changes.

Keywords:

clonal dynamics computational modeling therapeutic resistance tissue correlative studies tumor evolution

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Last Updated: Feb 9, 2026

Testing the Role of Multicopy Plasmids in the Evolution of Antibiotic Resistance

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Area of Science:

Oncology
Evolutionary Biology
Genomics

Background:

Tumor evolution is a complex process involving clonal dynamics and the emergence of therapeutic resistance.
Understanding tumor heterogeneity across space and time is crucial for effective cancer treatment.

Purpose of the Study:

To discuss distinct modes of tumor evolution and their implications for therapeutic strategies.
To explore methods for tracking tumor evolution and resistance, including spatial sampling, single-cell analysis, and circulating tumor DNA.
To highlight the potential of computational modeling in personalizing cancer treatment.

Main Methods:

Utilizing high-throughput sequencing to analyze pre- and post-treatment tumor samples.
Employing spatial tissue sampling schemes and single-cell analysis.
Leveraging circulating tumor DNA (ctDNA) analysis.
Integrating computational and mathematical modeling of tumor evolutionary dynamics.

Main Results:

Evidence for distinct modes of tumor evolution influencing therapeutic outcomes.
Demonstration of how clonal dynamics and resistance mechanisms can be elucidated through comparative sample analysis.
Identification of key molecular (epi)genomic, transcriptomic, and phenotypic changes during tumor progression.

Conclusions:

Characterizing tumor evolution through multi-omic profiling and advanced modeling can inform personalized treatment strategies.
Targeting tumor evolution and resistance mechanisms offers a promising avenue for novel therapeutic approaches.
Spatial and temporal monitoring of tumors is essential for adaptive treatment planning.