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

Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Tumor Progression02:07

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Exponential Equations for Modeling Growth01:26

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...
Cancer02:18

Cancer

Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.

You might also read

Related Articles

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

Sort by
Same author

Optimal Melanoma Treatment Protocols: a Bilinear Control Model.

Mathematical biosciences·2026
Same author

Population structure reverses selection of variants with proportionally scaled birth and death rates.

Nature communications·2025
Same author

Author Correction: Barcoded HIV-1 reveals viral persistence driven by clonal proliferation and distinct epigenetic patterns.

Nature communications·2025
Same author

Efficient mathematical methodology to determine multistep mutant burden in spatially growing cell populations.

PNAS nexus·2025
Same author

The functional form of the association between K-12 student performance and household income in U.S. school districts.

PloS one·2025
Same author

Detecting (the Absence of) Species Interactions in Microbial Ecological Systems.

Studies in applied mathematics (Cambridge, Mass.)·2025

Related Experiment Video

Updated: May 10, 2026

Measuring Growth and Gene Expression Dynamics of Tumor-Targeted S. Typhimurium Bacteria
08:11

Measuring Growth and Gene Expression Dynamics of Tumor-Targeted S. Typhimurium Bacteria

Published on: July 6, 2013

Tumor growth dynamics: insights into evolutionary processes.

Ignacio A Rodriguez-Brenes1, Natalia L Komarova, Dominik Wodarz

  • 1Department of Mathematics, University of California, Irvine, CA 92697, USA.

Trends in Ecology & Evolution
|July 3, 2013
PubMed
Summary
This summary is machine-generated.

Analyzing tumor growth patterns reveals key events driving cancer evolution. Understanding tumor kinetics alongside genetic changes offers insights into cancer progression and biology.

More Related Videos

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
10:24

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
07:42

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

Related Experiment Videos

Last Updated: May 10, 2026

Measuring Growth and Gene Expression Dynamics of Tumor-Targeted S. Typhimurium Bacteria
08:11

Measuring Growth and Gene Expression Dynamics of Tumor-Targeted S. Typhimurium Bacteria

Published on: July 6, 2013

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
10:24

Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation

Published on: September 19, 2019

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
07:42

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

Area of Science:

  • Oncology
  • Evolutionary Biology
  • Cancer Research

Background:

  • Identifying critical events driving tumor evolution and progression is essential for cancer research.
  • Tumor growth kinetics documentation is less common than molecular analysis of cancer cells.
  • Understanding the interplay between tumor growth dynamics and genetic alterations is vital.

Purpose of the Study:

  • To provide a historical overview of tumor growth patterns.
  • To classify tumor growth patterns into five basic categories.
  • To demonstrate how these classifications offer insights into tumor progression events.

Main Methods:

  • Historical literature review of tumor growth patterns.
  • Classification of observed tumor growth kinetics into distinct categories.
  • Application of an evolutionary model to illustrate insights from growth pattern analysis.

Main Results:

  • Tumor growth patterns can be historically summarized and categorized.
  • Five basic categories of tumor growth patterns were identified.
  • The proposed classification system provides a framework for understanding tumor progression.

Conclusions:

  • Analysis of tumor growth dynamics offers a valuable perspective on tumor biology and evolution.
  • Classifying tumor growth patterns can illuminate the genetic events driving cancer progression.
  • Further analysis integrating tumor kinetics and genetic changes is encouraged for a comprehensive understanding of cancer.