Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Woodstock Road, Oxford, OX2 6GG, United Kingdom.
Meiji University, School of Interdisciplinary Mathematical Sciences, 4-21-1 Nakano, Nakano-ku, Tokyo, 164-8525, Japan.
J Math Biol. 2021 Feb 23;82(4):21. doi: 10.1007/s00285-021-01570-w.
Although discrete approaches are increasingly employed to model biological phenomena, it remains unclear how complex, population-level behaviours in such frameworks arise from the rules used to represent interactions between individuals. Discrete-to-continuum approaches, which are used to derive systems of coarse-grained equations describing the mean-field dynamics of a microscopic model, can provide insight into such emergent behaviour. Coarse-grained models often contain nonlinear terms that depend on the microscopic rules of the discrete framework, however, and such nonlinearities can make a model difficult to mathematically analyse. By contrast, models developed using phenomenological approaches are typically easier to investigate but have a more obscure connection to the underlying microscopic system. To our knowledge, there has been little work done to compare solutions of phenomenological and coarse-grained models. Here we address this problem in the context of angiogenesis (the creation of new blood vessels from existing vasculature). We compare asymptotic solutions of a classical, phenomenological "snail-trail" model for angiogenesis to solutions of a nonlinear system of partial differential equations (PDEs) derived via a systematic coarse-graining procedure (Pillay et al. in Phys Rev E 95(1):012410, 2017. https://doi.org/10.1103/PhysRevE.95.012410 ). For distinguished parameter regimes corresponding to chemotaxis-dominated cell movement and low branching rates, both continuum models reduce at leading order to identical PDEs within the domain interior. Numerical and analytical results confirm that pointwise differences between solutions to the two continuum models are small if these conditions hold, and demonstrate how perturbation methods can be used to determine when a phenomenological model provides a good approximation to a more detailed coarse-grained system for the same biological process.
虽然离散方法越来越多地被用于模拟生物现象,但仍然不清楚在这些框架中,复杂的群体水平行为如何从用于表示个体之间相互作用的规则中产生。离散到连续的方法,用于推导出描述微观模型的平均场动力学的粗粒度方程系统,可以提供对这种涌现行为的深入了解。然而,粗粒度模型通常包含依赖于离散框架微观规则的非线性项,并且这种非线性可能使模型难以进行数学分析。相比之下,使用唯象方法开发的模型通常更容易研究,但与潜在的微观系统的联系更为模糊。据我们所知,很少有工作比较唯象和粗粒度模型的解。在这里,我们在血管生成(从现有脉管系统中创建新血管)的背景下解决这个问题。我们将经典的唯象“蜗牛迹”血管生成模型的渐近解与通过系统的粗粒化过程(Pillay 等人,Phys Rev E 95(1):012410,2017. https://doi.org/10.1103/PhysRevE.95.012410 )推导出的非线性偏微分方程组(PDEs)的解进行了比较。对于对应于趋化性主导的细胞运动和低分支率的区分参数范围,两个连续模型在主要阶次下都简化为相同的 PDEs 在域内。数值和分析结果证实,如果这些条件成立,两个连续模型的解之间的逐点差异很小,并演示了如何使用摄动方法来确定当唯象模型为相同的生物过程提供更详细的粗粒度系统的良好近似时。