[定量生物学] 胶质母细胞瘤Waddington景观模式的生成检测 对抗性网络 [推广有奖]

摘要翻译：
胶质母细胞瘤(GBM)是一种高度致死率和预后不良的疾病。它们的细胞异质性、治疗耐药性和自我更新等新特性主要归因于其群体的一个亚群--胶质母细胞瘤源性干细胞（glioblastoma-derived stem cells，GSCs)与其微环境之间的相互作用。在肿瘤学中，确定GSCs和成熟的、分化良好的GBM表型之间的发展轨迹的因果模式仍然是一个具有挑战性的问题。本文提出了从儿童GBM和成人GSCs的单细胞基因表达数据集推断吸引子动力学的复杂系统方法蓝图。这些算法包括Waddington景观重建、生成对抗网络和分形维数分析。在这里，我展示了一个Rossler样的奇怪吸引子，其分形维数约为1.7，出现在所有12个患者的GAN重建模式中。研究结果表明，一个奇怪的吸引子可能驱动着GBM在信号状态空间中的复杂动力学和适应性行为。
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英文标题：
《Pattern Detection on Glioblastoma's Waddington landscape via Generative
  Adversarial Networks》
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作者：
Abicumaran Uthamacumaran
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最新提交年份：
2021
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Physics        物理学
二级分类：Chaotic Dynamics        混沌动力学
分类描述：Dynamical systems, chaos, quantum chaos, topological dynamics, cycle expansions, turbulence, propagation
动力系统，混沌，量子混沌，拓扑动力学，循环展开，湍流，传播
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一级分类：Physics        物理学
二级分类：Biological Physics        生物物理学
分类描述：Molecular biophysics, cellular biophysics, neurological biophysics, membrane biophysics, single-molecule biophysics, ecological biophysics, quantum phenomena in biological systems (quantum biophysics), theoretical biophysics, molecular dynamics/modeling and simulation, game theory, biomechanics, bioinformatics, microorganisms, virology, evolution, biophysical methods.
分子生物物理、细胞生物物理、神经生物物理、膜生物物理、单分子生物物理、生态生物物理、生物系统中的量子现象（量子生物物理）、理论生物物理、分子动力学/建模与模拟、博弈论、生物力学、生物信息学、微生物、病毒学、进化论、生物物理方法。
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英文摘要：
  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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PDF链接：
https://arxiv.org/pdf/2107.04036