Collaborative Research: Computational Methods for Understanding the Influence of Cellular Geometry and Substructure on Signaling

合作研究：了解细胞几何形状和亚结构对信号传导影响的计算方法

• 批准号: 1902854
• 负责人: Samuel Isaacson
• 金额: $ 69.99万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1902854&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Methods; Understanding

To be able to predict and control the behavior of cells, it is necessary to understand how they detect, process and respond to external signals. This award will develop accurate and efficient numerical methods with which to study at the whole-cell scale how cells respond and process external signals. This will be done by developing new particle-based stochastic reaction-diffusion methods that allow the numerical simulation of the motion of, and reactions between, proteins on the surface of cells and within cells. High-resolution soft X-ray tomographic images of cells will be reconstructed to provide an accurate picture of the interfaces between immune cells. Geometries reconstructed from these images will then be used in computational modeling studies to investigate how the activation of immune cells depends on the structure of contact geometries between cells, and on physical properties of proteins involved in the signaling process. As immune cells play a key role in the body's response to pathogens and cancer, such studies have the potential to ultimately further our understanding and treatment of disease.This project investigates how both the shape of cell membranes, and cellular substructures within the cytosol, can modify the predicted dynamics of cell signaling pathways. This will be achieved by developing new particle-based stochastic reaction-diffusion (PBSRD) models in realistic cellular geometries of T cells, reconstructed from X-ray tomographic images. The studies will focus on T cell signaling pathways, where membrane-based signaling is critical for T cell activation in response to antigens, and highly regulated by the dynamics of cytosolic enzymes. In such pathways, membrane geometry is thought to play a major role through interactions of microvilli and filopodia with antigen presenting cells (APCs). The combination of modeling and imaging studies will give quantitative answers to the question of what the magnitude of these geometric effects are on T-cell signaling. Four-dimensional spatial stochastic models will be developed as they are necessary to accurately capture the dynamics of successfully functioning cellular signaling processes, in situations where cell shape, and internal substructure, can significantly influence the behavior of signaling pathways. The primary research objectives of this project are to: 1) Develop new PBSRD that incorporate the surface diffusion and reaction of molecules. 2) Develop efficient, exact numerical methods for sampling spatial jump processes associated with PBSRD models. 3) Conduct 3D X-ray tomographic imaging studies of T cells and T cells engaged with APCs to understand the variation in the shape of cells, organelles, and density of material within T cells. 4) Apply the new PBSRD methods in 3D geometries reconstructed from the imaging studies to investigate how cell shape and organelle barriers can influence the dynamics of T cell signaling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

为了能够预测和控制细胞的行为，有必要了解它们如何检测，处理和响应外部信号。该奖项将开发准确和有效的数值方法，用于在全细胞规模上研究细胞如何响应和处理外部信号。这将通过开发新的基于粒子的随机反应扩散方法来实现，该方法允许对细胞表面和细胞内蛋白质的运动和反应进行数值模拟。将重建细胞的高分辨率软X射线断层图像，以提供免疫细胞之间界面的准确图像。然后，从这些图像重建的几何形状将用于计算建模研究，以研究免疫细胞的激活如何取决于细胞之间接触几何形状的结构，以及参与信号传导过程的蛋白质的物理特性。由于免疫细胞在机体对病原体和癌症的反应中起着关键作用，这些研究有可能最终进一步加深我们对疾病的理解和治疗。本项目研究细胞膜的形状和细胞质内的细胞亚结构如何改变细胞信号传导途径的预测动力学。这将通过开发新的基于粒子的随机反应扩散（PBSRD）模型在现实的细胞几何形状的T细胞，重建从X射线断层图像。这些研究将集中在T细胞信号传导途径，其中基于膜的信号传导对于响应抗原的T细胞活化至关重要，并且受到胞质酶动力学的高度调节。在这些途径中，膜的几何形状被认为通过微绒毛和丝状伪足与抗原呈递细胞（APC）的相互作用发挥主要作用。建模和成像研究的结合将定量回答这些几何效应对T细胞信号传导的影响程度。四维空间随机模型将被开发，因为它们是必要的，以准确地捕捉成功运作的细胞信号传导过程的动态，在细胞形状和内部子结构的情况下，可以显着影响信号传导途径的行为。本计画的主要研究目标为：1）发展一种结合分子表面扩散与反应的PBSRD。2)开发有效的，精确的数值方法，用于采样与PBSRD模型相关的空间跳跃过程。3)对T细胞和与APC接合的T细胞进行3D X射线断层成像研究，以了解T细胞内细胞形状，细胞器和物质密度的变化。4)将新的PBSRD方法应用于从成像研究重建的3D几何结构中，以研究细胞形状和细胞器屏障如何影响T细胞信号传导的动力学。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

How Reaction-Diffusion PDEs Approximate the Large-Population Limit of Stochastic Particle Models

反应扩散偏微分方程如何逼近随机粒子模型的大总体极限

• DOI: 10.1137/20m1365429
• 发表时间: 2021
• 期刊: SIAM Journal on Applied Mathematics
• 影响因子: 1.9
• 作者: Isaacson, Samuel A.;Ma, Jingwei;Spiliopoulos, Konstantinos
• 通讯作者: Spiliopoulos, Konstantinos

Crowded transport within networked representations of complex geometries

• DOI: 10.1038/s42005-021-00732-y
• 发表时间: 2020-06
• 期刊: Communications Physics
• 影响因子: 5.5
• 作者: D. Wilson;Francis G. Woodhouse;M. Simpson;R. Baker

Detailed balance for particle models of reversible reactions in bounded domains

有界域中可逆反应粒子模型的详细平衡

• DOI: 10.1063/5.0085296
• 发表时间: 2022
• 期刊: The Journal of Chemical Physics
• 作者: Zhang, Ying;Isaacson, Samuel A.
• 通讯作者: Isaacson, Samuel A.

Mean Field Limits of Particle-Based Stochastic Reaction-Diffusion Models

基于粒子的随机反应扩散模型的平均场极限

• DOI: 10.1137/20m1365600
• 发表时间: 2022
• 期刊: SIAM journal on mathematical analysis
• 影响因子: 2
• 作者: Isaacson, Samuel A.;Ma, Jingwei;Spiliopoulos, Konstantinos
• 通讯作者: Spiliopoulos, Konstantinos