Uthamacumaran Abicumaran, Zenil Hector
Department of Physics, Concordia University, Montreal, QC, Canada.
Machine Learning Group, Department of Chemical Engineering and Biotechnology, The University of Cambridge, Cambridge, United Kingdom.
Front Oncol. 2022 Jul 25;12:850731. doi: 10.3389/fonc.2022.850731. eCollection 2022.
Cancers are complex adaptive diseases regulated by the nonlinear feedback systems between genetic instabilities, environmental signals, cellular protein flows, and gene regulatory networks. Understanding the cybernetics of cancer requires the integration of information dynamics across multidimensional spatiotemporal scales, including genetic, transcriptional, metabolic, proteomic, epigenetic, and multi-cellular networks. However, the time-series analysis of these complex networks remains vastly absent in cancer research. With longitudinal screening and time-series analysis of cellular dynamics, universally observed causal patterns pertaining to dynamical systems, may self-organize in the signaling or gene expression state-space of cancer triggering processes. A class of these patterns, strange attractors, may be mathematical biomarkers of cancer progression. The emergence of intracellular chaos and chaotic cell population dynamics remains a new paradigm in systems medicine. As such, chaotic and complex dynamics are discussed as mathematical hallmarks of cancer cell fate dynamics herein. Given the assumption that time-resolved single-cell datasets are made available, a survey of interdisciplinary tools and algorithms from complexity theory, are hereby reviewed to investigate critical phenomena and chaotic dynamics in cancer ecosystems. To conclude, the perspective cultivates an intuition for computational systems oncology in terms of nonlinear dynamics, information theory, inverse problems, and complexity. We highlight the limitations we see in the area of statistical machine learning but the opportunity at combining it with the symbolic computational power offered by the mathematical tools explored.
癌症是由遗传不稳定性、环境信号、细胞蛋白质流动和基因调控网络之间的非线性反馈系统所调节的复杂适应性疾病。理解癌症的控制论需要整合跨多维时空尺度的信息动力学，包括遗传、转录、代谢、蛋白质组学、表观遗传学和多细胞网络。然而，在癌症研究中，对这些复杂网络的时间序列分析仍然非常缺乏。通过对细胞动力学的纵向筛选和时间序列分析，与动力系统相关的普遍观察到的因果模式，可能会在癌症触发过程的信号传导或基因表达状态空间中自组织。这类模式中的一类，即奇怪吸引子，可能是癌症进展的数学生物标志物。细胞内混沌和混沌细胞群体动力学的出现仍然是系统医学中的一个新范式。因此，本文将混沌和复杂动力学作为癌细胞命运动力学的数学标志进行讨论。假设可以获得时间分辨的单细胞数据集，在此回顾了来自复杂性理论的跨学科工具和算法，以研究癌症生态系统中的关键现象和混沌动力学。总之，该观点从非线性动力学、信息论、反问题和复杂性方面培养了对计算系统肿瘤学的直觉。我们强调了我们在统计机器学习领域看到的局限性，但也强调了将其与所探索的数学工具提供的符号计算能力相结合的机会。
