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

What is Cancer?02:12

What is Cancer?

Cells and tissues must meticulously coordinate their activities for the normal functioning of the human body. Therefore, they exhibit socially responsible behavior - resting, growing, dividing, differentiating, or dying - for the organism’s benefit. Cancer arises when cells divide uncontrollably and invade other tissues or organs.
Although people have known about cancer for centuries, it was only in 1761 that Giovanni Morgagni of Padua performed a detailed autopsy of patients who died from...
What is Cancer?02:12

What is Cancer?

Cells and tissues must meticulously coordinate their activities for the normal functioning of the human body. Therefore, they exhibit socially responsible behavior - resting, growing, dividing, differentiating, or dying - for the organism’s benefit. Cancer arises when cells divide uncontrollably and invade other tissues or organs.
Although people have known about cancer for centuries, it was only in 1761 that Giovanni Morgagni of Padua performed a detailed autopsy of patients who died from...
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,...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
Cancers Originate from Somatic Mutations in a Single Cell02:21

Cancers Originate from Somatic Mutations in a Single Cell

Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...

You might also read

Related Articles

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

Sort by
Same author

Multiplexed P21/MCM-2 Detection Predicts Relapse and May Identify Tyrosine Kinase Inhibitor-Resistant Patients in Clear Cell Renal Cell Carcinoma.

Cancer research communications·2026
Same author

Long-Read Spatial Transcriptomics of Patient-Derived Clear Cell Renal Cell Carcinoma Organoids Identifies Heterogeneity and Transcriptional Remodelling Following NUC-7738 Treatment.

Cancers·2026
Same author

Knockout of the Intracellular Calcium Conducting Ion Channel Mitsugumin 23 (MG23) Protects Against Pressure Overload Induced Left Ventricular Hypertrophy and Cardiac Dysfunction.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

The significance of molecular heterogeneity in breast cancer batch correction and dataset integration.

Breast cancer research : BCR·2025
Same author

H&E-based MSI/MMR testing with AI in colorectal cancer: a multi-centred blinded evaluation.

NPJ digital medicine·2025
Same author

Endometrial whole-slide images dataset for detection of malignancy in endometrial biopsies.

GigaScience·2025
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Jun 9, 2026

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

Cancer systems biology.

Dana Faratian1, James L Bown, V Anne Smith

  • 1Breakthrough Research Unit, Centre for Research in Informatics and Systems Pathology (CRISP), University of Edinburgh, Edinburgh, UK. d.faratian@ed.ac.uk

Methods in Molecular Biology (Clifton, N.J.)
|September 9, 2010
PubMed
Summary
This summary is machine-generated.

Integrating diverse cancer data into systems biology models offers a promising approach to understand tumor complexity and predict treatment responses. Tailored computational methods are essential for analyzing heterogeneous biological data to advance cancer research.

More Related Videos

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
10:36

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Published on: March 17, 2016

Related Experiment Videos

Last Updated: Jun 9, 2026

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
10:36

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Published on: March 17, 2016

Area of Science:

  • Computational Biology
  • Systems Biology
  • Cancer Research

Background:

  • Cancer is a complex, heterogeneous disease impacting multiple biological levels.
  • High-throughput '-omics' data offer insights into tumor phenotype and drug response.
  • Integrating diverse data streams into systems biology models is crucial for understanding cancer.

Purpose of the Study:

  • To explore the potential of systems biology models in cancer research.
  • To discuss theoretical approaches and experimental methodologies for cancer data analysis.
  • To highlight the need for tailored computational methods for heterogeneous cancer data.

Main Methods:

  • Review of theoretical approaches: data-driven and process-driven models.
  • Discussion of experimental methodologies in cancer research and clinical application.
  • Emphasis on tailoring computational approaches to different data types.

Main Results:

  • Systems biology models can provide a predictive framework for tumor behavior and treatment response.
  • Heterogeneous and qualitative biological data necessitate diverse computational strategies.
  • Convergence of models may lead to accurate representations of human cancer.

Conclusions:

  • A single systems biology approach is insufficient for complex cancer data.
  • Tailored computational and mathematical methods are required for effective analysis.
  • Collaboration between biologists, clinicians, and computational modelers is vital for generating compatible data.