Cancer: Strange Attractors and Complexity

Cancer is a complex adaptive ecosystem and remains the lead cause of ‘disease’-related death in pediatric patients in North America. Machine learning, Network science, Fluid dynamics and Quantum Mechanics are hereby discussed as tools to advance oncology and cancer reprogramming. The fluid dynamics of cell-fate transitions, cancer pattern formation and invasion are reviewed in this paper. Cancer cell decision-making is investigated through dynamical systems and complexity theory. A fluid-dynamics grid-scheme based Deep Learning neural networks is proposed as the solution to identify strange attractors in time-series multi-omics (scRNA-Seq) data and time lapse imaging of cancer stem cell differentiation. Cancer is discussed within the context of two unsolved foundational problems in science: the three-dimensional Navier-Stokes equations global regularity, smoothness and existence and the P vs. NP problem.

• Publication year

  2019

• Venue

  Unknown venue

• Publication date

  2019-06-27

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.20944/PREPRINTS201906.0288.V1
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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