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arXiv 1202.4923q-bio.PEmath.APmath.DS
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分裂和标记结构种群增殖动力学的计算模型

A computational model for proliferation dynamics of division- and label-structured populations

J. Hasenauer, D. Schittler, F. Allgower

摘要:

在大多数生物学研究和过程中,细胞增殖和种群动力学起着至关重要的作用。由于这种普遍性,已经开发了大量的数学模型来描述这些过程。虽然最简单的模型只考虑总群体的大小,但其他模型考虑了细胞的分裂次数和标记。在这项工作中,我们提出了一个模型和计算框架进行对称细胞分裂增殖细胞群体。与现有的模型相比,该模型结合了离散的年龄结构和连续的标签动态。因此,它允许考虑分割数相关参数以及模型预测与标记实验的直接比较,例如,用羧基荧光素琥珀酰亚胺酯(CFSE)进行。我们证明,在温和的假设下,所得到的系统的耦合偏微分方程(PDE)可以分解成一个系统的常微分方程(ODE)和一组解耦的PDE,这大大减少了计算工作量。此外,偏微分方程的解析求解和常微分方程系统被截断,这使得使用低维常微分方程系统的复杂系统的标签分布的预测。除了建模的标记动力学,我们链接的标记诱导荧光的测量荧光,其中包括自发荧光。对于由此产生的数值上具有挑战性的卷积积分,我们提供了一个解析近似。这说明了通过建模和模拟增殖的人口与分裂数依赖的增殖率。

英文摘要:

In most biological studies and processes, cell proliferation and population dynamics play an essential role. Due to this ubiquity, a multitude of mathematical models has been developed to describe these processes. While the simplest models only consider the size of the overall populations, others take division numbers and labeling of the cells into account. In this work, we present a modeling and computational framework for proliferating cell population undergoing symmetric cell division. In contrast to existing models, the proposed model incorporates both, the discrete age structure and continuous label dynamics. Thus, it allows for the consideration of division number dependent parameters as well as the direct comparison of the model prediction with labeling experiments, e.g., performed with Carboxyfluorescein succinimidyl ester (CFSE). We prove that under mild assumptions the resulting system of coupled partial differential equations (PDEs) can be decomposed into a system of ordinary differential equations (ODEs) and a set of decoupled PDEs, which reduces the computational effort drastically. Furthermore, the PDEs are solved analytically and the ODE system is truncated, which allows for the prediction of the label distribution of complex systems using a low-dimensional system of ODEs. In addition to modeling of labeling dynamics, we link the label-induced fluorescence to the measure fluorescence which includes autofluorescence. For the resulting numerically challenging convolution integral, we provide an analytical approximation. This is illustrated by modeling and simulating a proliferating population with division number dependent proliferation rate.

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