School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
Cells. 2024 Aug 15;13(16):1358. doi: 10.3390/cells13161358.
Cell polarity refers to the asymmetric distribution of proteins and other molecules along a specified axis within a cell. Polarity establishment is the first step in many cellular processes. For example, directed growth or migration requires the formation of a cell front and back. In many cases, polarity occurs in the absence of spatial cues. That is, the cell undergoes symmetry breaking. Understanding the molecular mechanisms that allow cells to break symmetry and polarize requires computational models that span multiple spatial and temporal scales. Here, we apply a multiscale modeling approach to examine the polarity circuit of yeast. In addition to symmetry breaking, experiments revealed two key features of the yeast polarity circuit: bistability and rapid dismantling of the polarity site following a loss of signal. We used modeling based on ordinary differential equations (ODEs) to investigate mechanisms that generate these behaviors. Our analysis revealed that a model involving positive and negative feedback acting on different time scales captured both features. We then extend our ODE model into a coarse-grained reaction-diffusion equation (RDE) model to capture the spatial profiles of polarity factors. After establishing that the coarse-grained RDE model qualitatively captures key features of the polarity circuit, we expand it to more accurately capture the biochemical reactions involved in the system. We convert the expanded model to a particle-based model that resolves individual molecules and captures fluctuations that arise from the stochastic nature of biochemical reactions. Our models assume that negative regulation results from negative feedback. However, experimental observations do not rule out the possibility that negative regulation occurs through an incoherent feedforward loop. Therefore, we conclude by using our RDE model to suggest how negative feedback might be distinguished from incoherent feedforward regulation.
细胞极性是指蛋白质和其他分子沿着细胞内特定轴的不对称分布。极性建立是许多细胞过程的第一步。例如,定向生长或迁移需要形成细胞的前后端。在许多情况下,极性的形成是在没有空间线索的情况下发生的。也就是说,细胞经历了对称破缺。理解允许细胞打破对称并极化的分子机制需要跨越多个时空尺度的计算模型。在这里,我们应用多尺度建模方法来研究酵母的极性电路。除了对称破缺之外,实验还揭示了酵母极性电路的两个关键特征:双稳态和信号丢失后极性位点的快速拆卸。我们使用基于常微分方程 (ODE) 的建模来研究产生这些行为的机制。我们的分析表明,一个涉及正反馈和负反馈作用于不同时间尺度的模型捕捉到了这两个特征。然后,我们将我们的 ODE 模型扩展到一个粗粒化的反应-扩散方程 (RDE) 模型中,以捕捉极性因子的空间分布。在确定粗粒化 RDE 模型定性地捕获了极性电路的关键特征之后,我们将其扩展以更准确地捕获系统中涉及的生化反应。我们将扩展的模型转换为基于粒子的模型,该模型可以解析单个分子并捕获生化反应随机性引起的波动。我们的模型假设负反馈导致负调节。然而,实验观察结果并不排除负调节通过非相干前馈环发生的可能性。因此,我们使用我们的 RDE 模型得出结论,提出如何区分负反馈和非相干前馈调节。