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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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...
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,...

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Development of Compendium for Esophageal Squamous Cell Carcinoma
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Comparisons of robustness and sensitivity between cancer and normal cells by microarray data.

Liang-Hui Chu1, Bor-Sen Chen

  • 1Lab of Control and Systems Biology, National Tsing Hua University, Hsinchu, Taiwan.

Cancer Informatics
|March 5, 2009
PubMed
Summary

Cancer cells exhibit robustness but also extreme fragilities, a trade-off revealed by analyzing gene expression data. This study quantifies system robustness and sensitivity in cancer using computational methods and control theory.

Keywords:
feedback loops of p53robustness tradeoffsrobustness-based cancer drug designsensitivity analysis

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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Area of Science:

  • Systems Biology
  • Computational Biology
  • Cancer Research

Background:

  • Cancer is often viewed as a robust system, yet quantitative evidence for robustness trade-offs is limited.
  • Microarray data analysis is crucial for understanding gene expression signatures in cancer and for systems biology modeling.
  • Robustness and sensitivity are key concepts for characterizing system performance under perturbations.

Purpose of the Study:

  • To develop and apply computational methods from system and control theory to compare robustness and sensitivity between cancer and normal cells.
  • To provide quantitative evidence for robustness trade-offs in cancer.
  • To explore the extension of these methods for cancer drug design.

Main Methods:

  • Utilized system and control theory for computational analysis of microarray gene expression data.
  • Employed a linear stochastic model to measure robustness and sensitivity.
  • Investigated gene expression sensitivity to parameter perturbations and nonlinear effects in feedback loops.

Main Results:

  • Demonstrated robustness trade-offs in cancer, identifying it as a robust system with specific extreme fragilities.
  • Observed oscillations in the p53 feedback loops, providing insights into system dynamics.
  • Quantified differences in robustness and sensitivity between cancer and normal cells.

Conclusions:

  • Cancer exhibits a complex robustness profile, balancing overall stability with critical vulnerabilities.
  • The developed computational framework offers a novel approach to quantitatively assess cancer system properties.
  • This methodology has potential applications in developing targeted, robustness-based cancer therapies.