Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration
Combinatorial mathematical modelling approaches to interrogate rear retraction dynamics in 3D cell migration
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用于询问 3D 细胞迁移中后部回缩动力学的组合数学建模方法
DOI：
10.1101/2020.08.03.234021
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Hetmanski J
中科院分区：
文献类型：
--
作者：
Hetmanski J
细胞在三维微环境中的迁移是一个复杂的过程，它依赖于推拉机制中前缘推进力和后部回缩的协调活动。虽然突起的增强作用已被广泛研究，但导致细胞后部收缩的精确信号传导和机械事件的理解要少得多，特别是在生理3D细胞外基质（ECM）中。我们以前发现，在快速移动的细胞后方回缩是一个高度动态的过程，涉及精确的时空相互作用的mechanosensing通过小窝和信号通过RhoA。为了进一步询问后收缩的动力学，我们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（ODE）和（iii）随机模拟。这种多方面的方法的目的是双重的：首先，通过生成可检验的假设和预测，获得新的生物学洞察细胞后部动力学;其次，比较和对比不同的建模方法时，用于描述相同的，相对欠研究的系统。总的来说，我们的建模方法是相辅相成的，这表明这种多方面的方法比基于单一建模技术来询问生物系统的方法信息量更大。虽然布尔逻辑不能完全概括后部收缩信号的复杂性，但ODE模型可以做出合理的种群水平预测。随机模拟通过准确地模仿先前的实验发现并充当单细胞模拟器，增加了进一步的复杂性。我们的方法强调了CDK1在后部收缩中的意想不到的作用，我们通过实验证实了这一预测。此外，我们的模型导致了一种新的预测，关于潜在存在的一个“设定点”的局部刚度梯度，促进极化和快速后方回缩。
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.
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影响因子：
4.3
作者：
Hetmanski JH;Zindy E;Schwartz JM;Caswell PT
通讯作者：
Caswell PT
影响因子：
3.4
作者：
Ouaknin, Gaddiel Yonathan;Bar-Yoseph, Pinhas Zvi
通讯作者：
Bar-Yoseph, Pinhas Zvi
影响因子：
--
作者：
Nikonova, Elena;Tsyganov, Mikhail A.;Kholodenko, Boris N.
通讯作者：
Kholodenko, Boris N.
影响因子：
--
作者：
Ellerton EL
通讯作者：
Ellerton EL
影响因子：
21.3
作者：
Priya, Rashmi;Gomez, Guillermo A.;Yap, Alpha S.
通讯作者：
Yap, Alpha S.