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Related Concept Videos

Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

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Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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Cancer02:18

Cancer

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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.
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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...
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Introduction: Cancer Gene Networks.

Robert Clarke1

  • 1Department of Oncology, Georgetown Lombardi Comprehensive Cancer Center, W405A Research Building, 3970 Reservoir NW, Washington, DC, 20057, USA. clarker@georgetown.edu.

Methods in Molecular Biology (Clifton, N.J.)
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Summary

Systems biology uses quantitative multiscale models to understand complex biological systems, integrating high-throughput omics data for predictive insights. These models aid in identifying biomarkers and therapeutic targets in diseases like cancer.

Keywords:
Cancer gene networksComputational modelingHigh throughput omicsMathematical modelingQuantitative multiscale predictive modelsSubomicsSystems biology

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Area of Science:

  • Systems biology and computational modeling are rapidly emerging fields.
  • Focus on understanding complex signaling in cancer biology.

Background:

  • Systems biology defines systems as integrated networks of genes, variants, proteins, and molecules interacting to perform biochemical reactions.
  • Quantitative multiscale predictive models are central to managing biological system complexity.

Discussion:

  • Computational modeling integrates high-dimensional omics data (genomics, transcriptomics, proteomics) using machine learning.
  • Mathematical modeling uses differential equations for dynamic, semi-mechanistic models of low-dimensional data.
  • Integration of imaging technologies extends the scale of predictive modeling.

Key Insights:

  • Models predict molecular reactions, cellular phenotypes, population dynamics, and patient outcomes.
  • Goals include understanding system regulation and identifying predictive/prognostic biomarkers.
  • Network vulnerabilities can be identified as therapeutic targets, enabling drug repurposing or new drug development.

Outlook:

  • The volume provides practical and methodological insights for designing and interpreting systems biology studies.
  • Readers will find examples of various applications of predictive multiscale modeling.
  • Future work will continue to integrate diverse data types and scales for deeper biological understanding.