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Related Concept Videos

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,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
mTOR Signaling and Cancer Progression03:03

mTOR Signaling and Cancer Progression

The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
The mTOR pathway or the...
mTOR Signaling and Cancer Progression03:03

mTOR Signaling and Cancer Progression

The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
The mTOR pathway or the...

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Related Experiment Video

Updated: May 20, 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

Understanding cancer mechanisms through network dynamics.

Tammy M K Cheng1, Sakshi Gulati, Rudi Agius

  • 1Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, Lincoln's Inn Fields, London WC2A 3LY, UK.

Briefings in Functional Genomics
|July 20, 2012
PubMed
Summary
This summary is machine-generated.

Cancer research uses network analysis to understand complex cellular changes. This review highlights protein-protein interaction networks and simulation methods like Boolean networks and ordinary differential equations for studying tumor development.

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Generation of Heterogeneous Drug Gradients Across Cancer Populations on a Microfluidic Evolution Accelerator for Real-Time Observation
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Last Updated: May 20, 2026

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

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

Published on: September 19, 2019

Area of Science:

  • Systems biology
  • Computational biology
  • Cancer research

Background:

  • Cancer involves complex cellular system perturbations and dynamic variations.
  • High-throughput technologies like microarray and mass spectrometry aid in identifying disease-associated genes.
  • Computational approaches are crucial for understanding cancer mechanisms.

Purpose of the Study:

  • To review network approaches for studying cancer.
  • To provide an overview of protein-protein interaction networks in cancer.
  • To discuss network simulation methods for analyzing systemic perturbations.

Main Methods:

  • Review of network analysis techniques in cancer research.
  • Examination of protein-protein interaction networks.
  • Analysis of Boolean networks and ordinary differential equations for simulation.

Main Results:

  • Network analysis offers insights into tumor development and metastasis.
  • Protein-protein interaction networks are key to understanding systemic changes in cancer.
  • Boolean networks and ODEs are effective simulation methods for analyzing network perturbations.

Conclusions:

  • Network approaches are vital for cancer research and understanding disease mechanisms.
  • High-throughput data combined with network analysis advances cancer gene identification.
  • Simulation methods like Boolean networks and ODEs are essential tools for studying cancer systems biology.