Wellcome Trust Centre for Cell-Matrix Research, School of Biological Sciences, Faculty of Biology Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
PLoS Comput Biol. 2021 Mar 10;17(3):e1008213. doi: 10.1371/journal.pcbi.1008213. eCollection 2021 Mar.
Cell migration in 3D microenvironments is a complex process which depends on the coordinated activity of leading edge protrusive force and rear retraction in a push-pull mechanism. While the potentiation of protrusions has been widely studied, the precise signalling and mechanical events that lead to retraction of the cell rear are much less well understood, particularly in physiological 3D extra-cellular matrix (ECM). We previously discovered that rear retraction in fast moving cells is a highly dynamic process involving the precise spatiotemporal interplay of mechanosensing by caveolae and signalling through RhoA. To further interrogate the dynamics of rear retraction, we have adopted three distinct mathematical modelling approaches here based on (i) Boolean logic, (ii) deterministic kinetic ordinary differential equations (ODEs) and (iii) stochastic simulations. The aims of this multi-faceted approach are twofold: firstly to derive new biological insight into cell rear dynamics via generation of testable hypotheses and predictions; and secondly to compare and contrast the distinct modelling approaches when used to describe the same, relatively under-studied system. Overall, our modelling approaches complement each other, suggesting that such a multi-faceted approach is more informative than methods based on a single modelling technique to interrogate biological systems. Whilst Boolean logic was not able to fully recapitulate the complexity of rear retraction signalling, an ODE model could make plausible population level predictions. Stochastic simulations added a further level of complexity by accurately mimicking previous experimental findings and acting as a single cell simulator. Our approach highlighted the unanticipated role for CDK1 in rear retraction, a prediction we confirmed experimentally. Moreover, our models led to a novel prediction regarding the potential existence of a 'set point' in local stiffness gradients that promotes polarisation and rapid rear retraction.
细胞在 3D 微环境中的迁移是一个复杂的过程,依赖于前缘伸出力和后缘缩回力在推拉机制中的协调活动。虽然突起的增强作用已经得到了广泛的研究,但导致细胞后缘缩回的确切信号和力学事件在很大程度上还不太清楚,特别是在生理 3D 细胞外基质 (ECM) 中。我们之前发现,在快速运动的细胞中,后缘缩回是一个高度动态的过程,涉及质膜小窝的机械感受和 RhoA 信号的精确时空相互作用。为了进一步探究后缘缩回的动力学,我们在这里采用了三种不同的数学建模方法,基于 (i) 布尔逻辑,(ii) 确定性动力学常微分方程 (ODEs) 和 (iii) 随机模拟。这种多方面方法的目的有两个:首先,通过产生可测试的假设和预测,从新的角度深入了解细胞后缘动力学;其次,比较和对比当用于描述相同的、相对研究较少的系统时,不同的建模方法。总的来说,我们的建模方法相互补充,表明这种多方面的方法比基于单一建模技术来研究生物系统更具信息量。虽然布尔逻辑不能完全再现后缘缩回信号的复杂性,但 ODE 模型可以做出合理的群体水平预测。随机模拟通过准确模拟先前的实验结果并充当单个细胞模拟器,增加了一个额外的复杂性水平。我们的方法突出了 CDK1 在后缘缩回中的预期作用,这一预测我们通过实验得到了证实。此外,我们的模型还提出了一个新的预测,即在局部刚度梯度中存在一个“设定点”,可以促进极化和快速后缘缩回。