Cargando…

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

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 oft...

Descripción completa

Detalles Bibliográficos
Autor principal: West, Bruce J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013003/
https://www.ncbi.nlm.nih.gov/pubmed/36926098
http://dx.doi.org/10.3389/fnetp.2022.845495
_version_ 1784906725528174592
author West, Bruce J.
author_facet West, Bruce J.
author_sort West, Bruce J.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10013003
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-100130032023-03-15 The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks West, Bruce J. Front Netw Physiol Network Physiology 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. Frontiers Media S.A. 2022-03-24 /pmc/articles/PMC10013003/ /pubmed/36926098 http://dx.doi.org/10.3389/fnetp.2022.845495 Text en Copyright © 2022 West. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Network Physiology
West, Bruce J.
The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks
title The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks
title_full The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks
title_fullStr The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks
title_full_unstemmed The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks
title_short The Fractal Tapestry of Life: II Entailment of Fractional Oncology by Physiology Networks
title_sort fractal tapestry of life: ii entailment of fractional oncology by physiology networks
topic Network Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013003/
https://www.ncbi.nlm.nih.gov/pubmed/36926098
http://dx.doi.org/10.3389/fnetp.2022.845495
work_keys_str_mv AT westbrucej thefractaltapestryoflifeiientailmentoffractionaloncologybyphysiologynetworks
AT westbrucej fractaltapestryoflifeiientailmentoffractionaloncologybyphysiologynetworks