论文标题
在异质趋化和生长条件下微流体设备中的细胞动力学:一项数学研究
Cell dynamics in microfluidic devices under heterogeneous chemotaxis and growth conditions: a mathematical study
论文作者
论文摘要
由于由微型发行版中的时空趋化梯度驱动的细胞运动性的研究,我们开发了一个框架,用于为从下层差分方程模型的化学上层磁场中的细胞趋化域的位置,速度和细胞密度构建近似分析溶液。特别是,这种趋化波通常不是翻译不变的行进波,而是具有随时间发展的空间变化,甚至可能会根据趋化梯度的细节在及时来回振荡。分析框架利用了与趋化通量相比,无偏细胞扩散通量通常很小的观察结果,并首先开发并在一系列示例场景中进行了验证。考虑到化学吸引剂的全部动力学以及如何通过考虑一系列边界条件,该框架随后应用于更复杂的模型,以及如何在微电位内驱动和控制该框架。特别是,即使在所有情况下都不能构建溶液,也可以在分析上考虑多种情况,首先在复杂的趋化剂时空颞场中提供了对细胞运动的重要机制和特征的全球洞察力。这种分析解决方案还提供了对模型预测的快速评估的手段,并在计算要求中应用了有关理论模型和实验观察的研究,例如贝叶斯参数估计。
As motivated by studies of cellular motility driven by spatiotemporal chemotactic gradients in microdevices, we develop a framework for constructing approximate analytical solutions for the location, speed and cellular densities for cell chemotaxis waves in heterogeneous fields of chemoattractant from the underlying partial differential equation models. In particular, such chemotactic waves are not in general translationally invariant travelling waves, but possess a spatial variation that evolves in time, and may even may oscillate back and forth in time, according to the details of the chemotactic gradients. The analytical framework exploits the observation that unbiased cellular diffusive flux is typically small compared to chemotactic fluxes and is first developed and validated for a range of exemplar scenarios. The framework is subsequently applied to more complex models considering the full dynamics of the chemoattractant and how this may be driven and controlled within a microdevice by considering a range of boundary conditions. In particular, even though solutions cannot be constructed in all cases, a wide variety of scenarios can be considered analytically, firstly providing global insight into the important mechanisms and features of cell motility in complex spatiotemporal fields of chemoattractant. Such analytical solutions also provide a means of rapid evaluation of model predictions, with the prospect of application in computationally demanding investigations relating theoretical models and experimental observation, such as Bayesian parameter estimation.