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Cancer: A turbulence problem.

Abicumaran Uthamacumaran1

  • 1Concordia University, Montreal, QC, Canada.

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|November 3, 2020
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Summary
This summary is machine-generated.

Cancer dynamics involve chaotic gene expression and protein flows, challenging current models. This study reframes cancer as a complex system driven by chemical turbulence, impacting pattern formation and metastasis.

Keywords:
CancerChaosChemical turbulenceComplexityFractalsNonlinear dynamics

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Area of Science:

  • Oncology
  • Complex Systems Theory
  • Computational Biology

Background:

  • Cancers represent complex, adaptive ecosystems and are a leading cause of childhood disease-related death in North America.
  • Mathematical models of cancer dynamics require revision in light of emerging computational oncology and Deep Learning Healthcare approaches.
  • Recent findings suggest cancer-microenvironment interactions involve chaotic gene expressions and turbulent protein flows during pattern formation.

Purpose of the Study:

  • To revise mathematical models of cancer dynamics by incorporating complex systems theory.
  • To explore the role of chemical turbulence in cancer pattern formation, protein folding, and metastatic invasion.
  • To present cancer stem cells as strange attractors within the Waddington landscape framework.

Main Methods:

  • Application of complex systems theory to model cancer dynamics.
  • Analysis of chemical turbulence in relation to gene expression and protein flow.
  • Conceptual framework linking Waddington landscape to cancer stem cells.

Main Results:

  • Cancer pattern formation, protein folding, and metastatic invasion can be understood as processes driven by chemical turbulence.
  • The complex systems perspective provides a new framework for analyzing cancer-microenvironment interactions.
  • Cancer stem cells are proposed as analogous to strange attractors in developmental biology's Waddington landscape.

Conclusions:

  • Cancer dynamics are better understood through the lens of complex systems theory and chemical turbulence.
  • This approach offers novel insights into cancer progression, including metastasis and the role of cancer stem cells.
  • Revising computational oncology models with these principles is crucial for advancing cancer research and treatment.