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Related Concept Videos

The Evidence for Evolution02:55

The Evidence for Evolution

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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.
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Here we report the generation of Tre recombinase through directed, molecular evolution. Tre recombinase recognizes a pre-defined target sequence within the LTR sequences of the HIV-1 provirus, resulting in the excision and eradication of the provirus from infected human cells. While still in its infancy, directed molecular evolution will allow the creation of custom enzymes that will serve as tools of molecular surgery and molecular...
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Related Experiment Video

Updated: Jan 19, 2026

The Evidence for Evolution and Common Ancestor
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Estimating the predictability of cancer evolution.

Sayed-Rzgar Hosseini1,2, Ramon Diaz-Uriarte3, Florian Markowetz2

  • 1Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.

Bioinformatics (Oxford, England)
|September 13, 2019
PubMed
Summary

Cancer evolution is surprisingly predictable. A new computational method using mutational data shows that only a limited number of evolutionary paths are possible during cancer progression, aiding diagnosis and treatment.

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Area of Science:

  • Evolutionary biology
  • Computational biology
  • Cancer research

Background:

  • Predicting cancer evolution is crucial for diagnosis, prognosis, and treatment.
  • Traditional methods rely on fitness landscapes, which are difficult to determine in vivo for tumor cells.

Purpose of the Study:

  • To develop a computational method for quantifying cancer evolution predictability directly from mutational data.
  • To bypass the need for empirical fitness landscape measurements.

Main Methods:

  • Developed a computational method using conjunctive Bayesian networks (CBNs).
  • Validated the method using simulated data from over 200 fitness landscapes.
  • Applied the approach to driver mutation data from TCGA and MSK-IMPACT cohorts for 15 cancer types.

Main Results:

  • The CBN-based predictability strongly correlates with fitness landscape-based predictability under specific assumptions.
  • The statistical framework provides robust and scalable quantification of evolutionary predictability.
  • Cancer evolution was found to be remarkably predictable, with a small fraction of feasible evolutionary trajectories.

Conclusions:

  • Cancer evolution exhibits significant predictability.
  • The developed method offers a robust way to quantify this predictability from mutational data.
  • Findings have implications for improving cancer diagnosis, prognosis, and treatment strategies.