Phenotype structuring in collective cell migration: a tutorial of mathematical models and methods.

IF 2.2 4区 数学 Q2 BIOLOGY
Tommaso Lorenzi, Kevin J Painter, Chiara Villa
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引用次数: 0

Abstract

Populations are heterogeneous, deviating in numerous ways. Phenotypic diversity refers to the range of traits or characteristics across a population, where for cells this could be the levels of signalling, movement and growth activity, etc. Clearly, the phenotypic distribution - and how this changes over time and space - could be a major determinant of population-level dynamics. For instance, across a cancerous population, variations in movement, growth, and ability to evade death may determine its growth trajectory and response to therapy. In this review, we discuss how classical partial differential equation (PDE) approaches for modelling cellular systems and collective cell migration can be extended to include phenotypic structuring. The resulting non-local models - which we refer to as phenotype-structured partial differential equations (PS-PDEs) - form a sophisticated class of models with rich dynamics. We set the scene through a brief history of structured population modelling, and then review the extension of several classic movement models - including the Fisher-KPP and Keller-Segel equations - into a PS-PDE form. We proceed with a tutorial-style section on derivation, analysis, and simulation techniques. First, we show a method to formally derive these models from underlying agent-based models. Second, we recount travelling waves in PDE models of spatial spread dynamics and concentration phenomena in non-local PDE models of evolutionary dynamics, and combine the two to deduce phenotypic structuring across travelling waves in PS-PDE models. Third, we discuss numerical methods to simulate PS-PDEs, illustrating with a simple scheme based on the method of lines and noting the finer points of consideration. We conclude with a discussion of future modelling and mathematical challenges.

集体细胞迁移中的表型结构:数学模型和方法教程。
人口是异质的,在许多方面存在偏差。表型多样性指的是整个群体的特征或特征的范围,对于细胞来说,这可能是信号传导、运动和生长活动等的水平。显然,表型分布——以及这种分布如何随时间和空间变化——可能是种群水平动态的主要决定因素。例如,在癌症人群中,运动、生长和逃避死亡能力的变化可能决定其生长轨迹和对治疗的反应。在这篇综述中,我们讨论了经典的偏微分方程(PDE)方法如何建模细胞系统和集体细胞迁移可以扩展到包括表型结构。由此产生的非局部模型-我们称之为表型结构偏微分方程(PS-PDEs) -形成了一类具有丰富动力学的复杂模型。我们通过结构化人口模型的简史来设置场景,然后回顾几个经典运动模型的扩展-包括Fisher-KPP和Keller-Segel方程-到PS-PDE形式。我们将继续进行有关推导、分析和模拟技术的教程式部分。首先,我们展示了一种从底层基于代理的模型正式派生这些模型的方法。其次,我们叙述了空间传播动力学的PDE模型中的行波和进化动力学的非局部PDE模型中的集中现象,并将两者结合起来推断PS-PDE模型中跨行波的表型结构。第三,我们讨论了模拟PS-PDEs的数值方法,用基于线法的简单方案进行了说明,并指出了需要考虑的细节。最后,我们讨论了未来的建模和数学挑战。
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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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