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生命的分形织锦:二、生理网络对分数肿瘤学的蕴含

The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks.

作者信息

West Bruce J

机构信息

Center for Nonlinear Science, Univesity of North Texas, Denton, TX, United States.

Office for Research and Innovation, North Carolina State University, Rayleigh, NC, United States.

出版信息

Front Netw Physiol. 2022 Mar 24;2:845495. doi: 10.3389/fnetp.2022.845495. eCollection 2022.

Abstract

This is an essay advocating the efficacy of using the (noninteger) fractional calculus (FC) for the modeling of complex dynamical systems, specifically those pertaining to biomedical phenomena in general and oncological phenomena in particular. Herein we describe how the integer calculus (IC) is often incapable of describing what were historically thought to be simple linear phenomena such as Newton's law of cooling and Brownian motion. We demonstrate that even linear dynamical systems may be more accurately described by fractional rate equations (FREs) when the experimental datasets are inconsistent with models based on the IC. The Network Effect is introduced to explain how the collective dynamics of a complex network can transform a many-body noninear dynamical system modeled using the IC into a set of independent single-body fractional stochastic rate equations (FSREs). Note that this is not a mathematics paper, but rather a discussion focusing on the kinds of phenomena that have historically been approximately and improperly modeled using the IC and how a FC replacement of the model better explains the experimental results. This may be due to hidden effects that were not anticapated in the IC model, or to an effect that was acknowledged as possibly significant, but beyond the mathematical skills of the investigator to Incorporate into the original model. Whatever the reason we introduce the FRE used to describe mathematical oncology (MO) and review the quality of fit of such models to tumor growth data. The analytic results entailed in MO using ordinary diffusion as well as fractional diffusion are also briefly discussed. A connection is made between a time-dependent fractional-order derivative, technically called a distributed-order parameter, and the multifractality of time series, such that an observed multifractal time series can be modeled using a FRE with a distributed fractional-order derivative. This equivalence between multifractality and distributed fractional derivatives has not received the recognition in the applications literature we believe it warrants.

摘要

这是一篇主张使用(非整数)分数阶微积分(FC)对复杂动力系统进行建模的功效的文章,具体而言,这些系统一般涉及生物医学现象,特别是肿瘤学现象。在此我们描述了整数微积分(IC)通常如何无法描述那些在历史上被认为是简单线性现象的情况,比如牛顿冷却定律和布朗运动。我们证明,当实验数据集与基于IC的模型不一致时,分数阶速率方程(FREs)可能会更准确地描述线性动力系统。引入网络效应来解释复杂网络的集体动力学如何将一个使用IC建模的多体非线性动力系统转化为一组独立的单体分数阶随机速率方程(FSREs)。请注意,这不是一篇数学论文,而是一篇讨论文章,重点关注那些在历史上一直使用IC进行近似且不恰当建模的现象类型,以及用FC替换该模型如何能更好地解释实验结果。这可能是由于IC模型中未预料到的隐藏效应,或者是由于一种被认为可能很重要但超出研究者数学能力而无法纳入原始模型的效应。无论原因是什么,我们引入用于描述数学肿瘤学(MO)的FRE,并回顾此类模型对肿瘤生长数据的拟合质量。还简要讨论了MO中使用普通扩散以及分数阶扩散所产生的分析结果。在一个与时间相关的分数阶导数(技术上称为分布阶参数)和时间序列的多重分形性之间建立了联系,这样一个观测到的多重分形时间序列就可以使用具有分布分数阶导数的FRE来建模。我们认为,多重分形性和分布分数阶导数之间的这种等价性在应用文献中尚未得到应有的认可。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a8f8/10013003/da00289d184a/fnetp-02-845495-g001.jpg

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