Preprint Article Version 1 This version is not peer-reviewed

Cancer: Strange Attractors and Complexity

Version 1 : Received: 26 June 2019 / Approved: 27 June 2019 / Online: 27 June 2019 (11:36:18 CEST)

How to cite: Uthamacumaran, A. Cancer: Strange Attractors and Complexity. Preprints 2019, 2019060288 (doi: 10.20944/preprints201906.0288.v1). Uthamacumaran, A. Cancer: Strange Attractors and Complexity. Preprints 2019, 2019060288 (doi: 10.20944/preprints201906.0288.v1).

Abstract

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.

Subject Areas

Cancer; Turbulence; Complexity; Machine Learning; Chaos; Reprogramming

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