Pattern Detection on Glioblastoma's Waddington landscape via Generative Adversarial Networks
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by
Abicumaran Uthamacumaran
2021
Abstract
Glioblastoma (GBM) is a highly morbid and lethal disease with poor prognosis.
Their emergent properties such as cellular heterogeneity, therapy resistance,
and self-renewal are largely attributed to the interactions between a subset of
their population known as glioblastoma-derived stem cells (GSCs) and their
microenvironment. Identifying causal patterns in the developmental trajectories
between GSCs and the mature, well-differentiated GBM phenotypes remains a
challenging problem in oncology. The paper presents a blueprint of complex
systems approaches to infer attractor dynamics from the single-cell gene
expression datasets of pediatric GBM and adult GSCs. These algorithms include
Waddington landscape reconstruction, Generative Adversarial Networks, and
fractal dimension analysis. Here I show, a Rossler-like strange attractor with
a fractal dimension of roughly 1.7 emerged in the GAN-reconstructed patterns of
all twelve patients. The findings suggest a strange attractor may be driving
the complex dynamics and adaptive behaviors of GBM in signaling state-space.
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