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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,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A higher...

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Related Experiment Video

Updated: Jun 19, 2026

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
10:51

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Published on: April 23, 2021

Cancer systems biology: a network modeling perspective.

Pamela K Kreeger1, Douglas A Lauffenburger

  • 1Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA.

Carcinogenesis
|October 29, 2009
PubMed
Summary

Computational modeling aids in understanding complex cancer biology. This approach helps identify key pathways, mutation impacts, and therapeutic effects for better cancer treatment strategies.

Area of Science:

  • Oncology
  • Computational Biology
  • Systems Biology

Background:

  • Cancer is a complex, heterogeneous disease involving dysregulated cellular processes.
  • Genetic mutations and environmental factors intricately alter molecular networks in cancer.
  • High-throughput data (genomics, transcriptomics, proteomics, metabolomics) present interpretation challenges.

Purpose of the Study:

  • To discuss the application of computational modeling in cancer research.
  • To elucidate critical pathways in tumor formation and progression.
  • To understand the impact of mutations and therapeutics on cancer biology.

Main Methods:

  • Review of computational modeling approaches for cancer pathway analysis.
  • Integration of multi-omics data for systems-level understanding.

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

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Last Updated: Jun 19, 2026

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System
10:51

Modeling Breast Cancer in Human Breast Tissue using a Microphysiological System

Published on: April 23, 2021

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

  • Analysis of how computational models can predict therapeutic responses.

    • Main Results:

    • Computational modeling offers actionable insights into multivariate cancer dysregulation.
    • It helps identify critical molecular pathways driving tumorigenesis.
    • Models can predict the consequences of genetic mutations and therapeutic interventions.

    Conclusions:

    • Computational modeling is essential for deciphering complex cancer biology.
    • It provides a framework for understanding mutation impacts and guiding therapeutic development.
    • This approach enhances the interpretation of high-throughput data for clinical applications.