通过常微分方程模型揭示佩托悖论的内生条件。

IF 2.2 4区 数学 Q2 BIOLOGY
Haichun Kan, Yu Chen
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引用次数: 0

摘要

癌症是一种与细胞突变密切相关的疾病,人们普遍认为癌症与一个物种的细胞数量和寿命呈正相关。尽管有这种假设,但在不同物种中观察到的癌症发病率的一致性,即 "佩托悖论"(Peto's paradox),提出了一个难题。本研究认识到肿瘤的发展并不完全依赖于癌细胞,而是涉及各种细胞类型之间错综复杂的相互作用,因此采用了洛特卡-伏特拉(LV)常微分方程模型来分析免疫环境中癌细胞的演化和癌症发病率。因此,本研究揭示了佩托悖论中不存在相关性的充分条件,并通过应用非维度化,将常微分方程模型中细胞群变化的特征时间间隔与物种的细胞周期时间间隔进行类比,深入探讨了癌症发病率在不同物种间公平分布的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Revealing endogenous conditions for Peto's paradox via an ordinary differential equation model.

Revealing endogenous conditions for Peto's paradox via an ordinary differential equation model.

Cancer, a disease intimately linked to cellular mutations, is commonly believed to exhibit a positive association with the cell count and lifespan of a species. Despite this assumption, the observed uniformity in cancer rates across species, referred to as the Peto's paradox, presents a conundrum. Recognizing that tumour progression is not solely dependent on cancer cells but involves intricate interactions among various cell types, this study employed a Lotka-Volterra (LV) ordinary differential equation model to analyze the evolution of cancerous cells and the cancer incidence in an immune environment. As a result, this study uncovered the sufficient conditions underlying the absence of correlation in Peto's paradox and provide insights into the reasons for the equitable distribution of cancer incidence across diverse species by applying nondimensionalization and drawing an analogy between the characteristic time interval for the variation of cell populations in the ODE model and that of cell cycles of a species.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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