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 Experiment Videos

Stochastic tunnels in evolutionary dynamics.

Yoh Iwasa1, Franziska Michor, Martin A Nowak

  • 1Department of Biology, Kyushu University, Fukuoka 812-8581, Japan. yiwasscb@mbox.nc.kyushu-u.ac.jp

Genetics
|April 15, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Targeted degradation of MDM2 overcomes feedback regulation of p53 signaling in Merkel cell carcinoma models.

The Journal of clinical investigation·2026
Same author

ESR1 mutations and CDK4/6 inhibitor choice shape clonal selection and adaptive cell states during acquired resistance.

Genome medicine·2026
Same author

A pan-cancer single-cell analysis of intratumoral copy number diversity and evolution.

Cancer discovery·2026
Same author

H3K27M-driven hypertranscription leads to a new targetable dependency in diffuse midline gliomas.

bioRxiv : the preprint server for biology·2026
Same author

Radiation Oncology-Biology Integration Network: Bridging the Gap between Biological Research and Clinical Practice.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

HER2 heterogeneous breast cancer models reveal novel therapeutic targets and subclonal dynamics during evolution to resistance to HER2-targeted therapies.

Cancer discovery·2026

Cancer cells can evolve to skip intermediate stages, a phenomenon called stochastic tunneling. This occurs when a population transitions directly from type 0 to type 2, bypassing type 1, with implications for tumor suppressor gene elimination.

Area of Science:

  • Evolutionary biology
  • Cancer genetics
  • Mathematical modeling

Background:

  • Somatic evolution in cancer involves a finite population of replicating cells undergoing mutations.
  • Sequential mutations (type 0 to 1, type 1 to 2) occur without back mutation, starting from a homogeneous type 0 population.
  • Mutant populations (type 1) can emerge, go extinct, or reach fixation, potentially generating subsequent mutant types (type 2).

Purpose of the Study:

  • To investigate the phenomenon of "stochastic tunneling" in cancer cell evolution.
  • To calculate the exact rate of stochastic tunneling under specific fitness conditions.
  • To discuss the implications of stochastic tunneling for cancer development and genetic instability.

Main Methods:

  • Modeling stochastic dynamics of cell populations with sequential mutations.

Related Experiment Videos

  • Analyzing transitions between homogeneous populations when mutation rates are low relative to population size.
  • Calculating the exact rate of stochastic tunneling for scenarios where type 1 fitness is less than or equal to type 0 fitness.

    • Main Results:

    • A "stochastic tunnel" is defined as a population transition from all type 0 to all type 2 without an intermediate all type 1 state.
    • The exact rate of stochastic tunneling was calculated for type 1 being as fit as or less fit than type 0, with type 2 being the fittest.
    • The study provides a theoretical framework for understanding evolutionary pathways in finite populations.

    Conclusions:

    • Stochastic tunneling offers a potential mechanism for the rapid elimination of tumor suppressor genes and activation of genetic instability in cancer.
    • The theoretical framework developed is applicable to cancer genetics but represents a general phenomenon in evolutionary dynamics.
    • Understanding stochastic tunnels can inform strategies for cancer treatment and prevention by considering non-linear evolutionary trajectories.