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生物质生长中机械生物学过程的多孔弹性混合模型:理论及其在组织工程中的应用

A poroelastic mixture model of mechanobiological processes in biomass growth: theory and application to tissue engineering.

作者信息

Sacco Riccardo, Causin Paola, Lelli Chiara, Raimondi Manuela T

机构信息

1Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.

2Dipartimento di Matematica "F. Enriques", Università degli Studi di Milano, Via Saldini 50, 20133 Milan, Italy.

出版信息

Meccanica. 2017;52(14):3273-3297. doi: 10.1007/s11012-017-0638-9. Epub 2017 Feb 20.

DOI:10.1007/s11012-017-0638-9
PMID:32009677
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6959421/
Abstract

In this article we propose a novel mathematical description of biomass growth that combines poroelastic theory of mixtures and cellular population models. The formulation, potentially applicable to general mechanobiological processes, is here used to study the engineered cultivation in bioreactors of articular chondrocytes, a process of Regenerative Medicine characterized by a complex interaction among spatial scales (from nanometers to centimeters), temporal scales (from seconds to weeks) and biophysical phenomena (fluid-controlled nutrient transport, delivery and consumption; mechanical deformation of a multiphase porous medium). The principal contribution of this research is the inclusion of the concept of cellular "force isotropy" as one of the main factors influencing cellular activity. In this description, the induced cytoskeletal tensional states trigger signalling transduction cascades regulating functional cell behavior. This mechanims is modeled by a parameter which estimates the influence of local force isotropy by the norm of the deviatoric part of the total stress tensor. According to the value of the estimator, isotropic mechanical conditions are assumed to be the promoting factor of extracellular matrix production whereas anisotropic conditions are assumed to promote cell proliferation. The resulting mathematical formulation is a coupled system of nonlinear partial differential equations comprising: conservation laws for mass and linear momentum of the growing biomass; advection-diffusion-reaction laws for nutrient (oxygen) transport, delivery and consumption; and kinetic laws for cellular population dynamics. To develop a reliable computational tool for the simulation of the engineered tissue growth process the nonlinear differential problem is numerically solved by: (1) temporal semidiscretization; (2) linearization via a fixed-point map; and (3) finite element spatial approximation. The biophysical accuracy of the mechanobiological model is assessed in the analysis of a simplified 1D geometrical setting. Simulation results show that: (1) isotropic/anisotropic conditions are strongly influenced by both maximum cell specific growth rate and mechanical boundary conditions enforced at the interface between the biomass construct and the interstitial fluid; (2) experimentally measured features of cultivated articular chondrocytes, such as the early proliferation phase and the delayed extracellular matrix production, are well described by the computed spatial and temporal evolutions of cellular populations.

摘要

在本文中,我们提出了一种生物质生长的新颖数学描述,它结合了混合物的多孔弹性理论和细胞群体模型。该公式可能适用于一般的力学生物学过程,在此用于研究关节软骨细胞在生物反应器中的工程培养,这是一种再生医学过程,其特征在于空间尺度(从纳米到厘米)、时间尺度(从秒到周)和生物物理现象(流体控制的营养物质运输、递送和消耗;多相多孔介质的机械变形)之间的复杂相互作用。本研究的主要贡献在于将细胞“力各向同性”的概念作为影响细胞活性的主要因素之一纳入其中。在这种描述中,诱导的细胞骨架张力状态触发调节细胞功能行为的信号转导级联反应。这种机制由一个参数建模,该参数通过总应力张量偏量部分的范数来估计局部力各向同性的影响。根据估计器的值,各向同性机械条件被假定为细胞外基质产生的促进因素,而异向性条件被假定为促进细胞增殖。所得的数学公式是一个非线性偏微分方程的耦合系统,包括:生长生物质的质量和线性动量守恒定律;营养物质(氧气)运输、递送和消耗的对流 - 扩散 - 反应定律;以及细胞群体动力学的动力学定律。为了开发一种用于模拟工程组织生长过程的可靠计算工具,通过以下方式对非线性微分问题进行数值求解:(1) 时间半离散化;(2) 通过不动点映射进行线性化;(3) 有限元空间近似。在对简化的一维几何设置的分析中评估了力学生物学模型的生物物理准确性。模拟结果表明:(1) 各向同性/各向异性条件受到最大细胞比生长速率和在生物质构建体与间质液界面处施加的机械边界条件的强烈影响;(2) 计算得到的细胞群体的空间和时间演变很好地描述了培养的关节软骨细胞的实验测量特征,如早期增殖阶段和延迟的细胞外基质产生。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/922ba8c2570f/11012_2017_638_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/42aa916cf4ee/11012_2017_638_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/bd4c0cb1c3bb/11012_2017_638_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/b1989bfe8e89/11012_2017_638_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/b226a5d2dc51/11012_2017_638_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/642306414942/11012_2017_638_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/5e319a009162/11012_2017_638_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/6a24d377116b/11012_2017_638_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/a62c7bab0015/11012_2017_638_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/af55a4a59dc5/11012_2017_638_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/6ae020a8a90b/11012_2017_638_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39d3/6959421/657a72eb753e/11012_2017_638_Fig15_HTML.jpg
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