论文标题
基于随机单粒子的基于嵌入细胞形态计算模型的细胞信号传导的模拟
Stochastic single-particle based simulations of cellular signaling embedded into computational models of cellular morphology
论文作者
论文摘要
细胞表现出各种不同的形状。这种多样性对试图阐明细胞几何作用在调节细胞生理和行为的角色上发挥作用的计算方法提出了挑战。模拟平台Simmune能够将信号通路的计算表示形式嵌入到现实的细胞形态模型中。但是，Simmune当前考虑细胞几何形状的方法仅限于反应扩散过程的确定性模型，因此提供了一个粗粒的描述，忽略了随机的局部波动。在这里，我们提出了Simmune的扩展，该扩展通过采用平滑且无网格的细胞几何形状的替代计算表示来消除这些局限性。这些特征使得对细胞生物化学的完全随机，空间分辨的描述成为可能。替代计算表示与Simmune当前指定分子相互作用的方法兼容。这意味着使用该方法的建模者需要创建一个细胞生物化学和形态的模型，以便能够将其用于确定性和随机模拟。
Cells exhibit a wide variety of different shapes. This diversity poses a challenge for computational approaches that attempt to shed light on the role cell geometry plays in regulating cell physiology and behavior. The simulation platform Simmune is capable of embedding the computational representation of signaling pathways into realistic models of cellular morphology. However, Simmune's current approach to account for the cell geometry is limited to deterministic models of reaction-diffusion processes, thus providing a coarse-grained description that ignores stochastic local fluctuations. Here we present an extension of Simmune that removes these limitations by employing an alternative computational representation of cellular geometry that is smooth and grid-free. These features make it possible to incorporate a fully stochastic, spatially resolved description of the cellular biochemistry. The alternative computational representation is compatible with Simmune's current approach for specifying molecular interactions. This means that a modeler using the approach needs to create a model of cellular biochemistry and morphology only once to be able to use it for both, deterministic and stochastic simulations.
