Collaborative Research: Computational Modeling of How Living Cells Utilize Liquid-Liquid Phase Separation to Organize Chemical Compartments

合作研究：活细胞如何利用液-液相分离来组织化学区室的计算模型

• 批准号: 1815921
• 负责人: Qi Wang
• 金额: $ 15万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815921&HistoricalAwards=false
• 关键词: Collaborative; Research; Computational; Modeling; How

Eukaryotic cells have evolved multiple mechanisms for sequestering and maintaining localized chemical or molecular concentrations. The most obvious is a physical membrane, such as the cell membrane that separates the cytoplasm from its surrounding environment or the nuclear membrane that confines chromosomal DNA within the nucleus. Mechanisms for compartmentalization are essential as they override diffusive smoothing of concentration gradients that would otherwise homogenize cellular contents and fail to allow spatial regulation of critical cellular processes. A recently identified and current intense focus in cell biology is on chemical compartments that form in the absence of physical membranes. This project focuses on a specific example: the binding of cytoplasmic proteins and RNAs into complexes that form protein-rich droplets by way of liquid-liquid phase separation (LLPS). By bringing together mathematical, computational, and biological scientists, the investigators aim to develop a general computational modeling platform to study cytoplasmic droplets and their spatial distributions that arise from LLPS. The aim is to understand mechanistically how these compartments establish and preserve cytoplasmic heterogeneity in mRNA localization and expression in live cells, and the molecular species, complexes, and kinetic timescales that are responsible. By applications of this platform to other live cells, there is the potential to understand the essential cell-specific molecular ingredients and chemical kinetics for LLPS, thereby contributing to understanding of the diversity of intracellular compartmentalization across cell biology. There is a rich history in cell biology of the study of membranes and their role in establishing extracellular and intracellular chemical compartments. Yet, relatively little is known about how molecular proteins, organelles, and chromosomal DNA, within the cytoplasm or within the nucleus, chemically interact and self-organize to create, sustain, and evolve localized chemical and macromolecular compartments in the absence of physical membranes. Armed with resolved spatial and temporal experimental data of primary molecular species and species complexes, the investigators in this project focus on three specific aims. 1. A computational modeling platform to explore the input space of primary molecular (proteins, RNAs, protein-RNA complexes) and microscopic (nuclei, membranes) species, chemical species affinities, and spatial confinement conditions. This platform will produce a phase diagram of outcomes that mimics live cell data (dynamic self-organization of complexes and molecular species, droplet formation due to liquid-liquid phase separation), and that reveals sufficient ingredients and interactions for membrane-less, intracellular chemical compartments, and their robustness. 2. By way of coupled stochastic and continuum modeling, conditioning on ex vivo and in vivo experimental data, to discover sufficient molecular species, complexes, and hidden chemical affinities that reproduce the chemical compartmentalization of live cells. 3. To extend numerical tools for multiphase modeling to accommodate strong fluctuations and out-of-equilibrium behavior driven by chemical kinetics, viscoelasticity of droplets, and induced flow by liquid-liquid phase separation.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.

真核细胞已经进化出多种隔离和维持局部化学或分子浓度的机制。最明显的是物理膜，例如将细胞质与其周围环境分开的细胞膜或将染色体 DNA 限制在细胞核内的核膜。区室化机制至关重要，因为它们超越了浓度梯度的扩散平滑，否则浓度梯度会均匀化细胞内容物，并且无法实现关键细胞过程的空间调节。最近发现的细胞生物学的一个热点焦点是在没有物理膜的情况下形成的化学区室。 该项目重点关注一个具体示例：细胞质蛋白和 RNA 结合成复合物，通过液-液相分离 (LLPS) 形成富含蛋白质的液滴。通过汇集数学、计算和生物科学家，研究人员旨在开发一个通用计算模型平台来研究 LLPS 产生的细胞质液滴及其空间分布。 目的是从机制上了解这些区室如何在活细胞中的 mRNA 定位和表达中建立和保持细胞质异质性，以及负责的分子种类、复合物和动力学时间尺度。通过将该平台应用于其他活细胞，有可能了解 LLPS 的基本细胞特异性分子成分和化学动力学，从而有助于了解整个细胞生物学中细胞内区室化的多样性。细胞生物学对膜及其在建立细胞外和细胞内化学区室中的作用的研究有着丰富的历史。然而，对于细胞质或细胞核内的分子蛋白、细胞器和染色体 DNA 如何在没有物理膜的情况下进行化学相互作用和自组织以创建、维持和进化局部化学和大分子区室，人们知之甚少。凭借主要分子物种和物种复合体的解析空间和时间实验数据，该项目的研究人员专注于三个具体目标。 1. 一个计算建模平台，用于探索主要分子（蛋白质、RNA、蛋白质-RNA 复合物）和微观（细胞核、膜）物种的输入空间、化学物种亲和力和空间限制条件。 该平台将生成模拟活细胞数据的结果相图（复合物和分子种类的动态自组织、液-液相分离导致的液滴形成），并揭示无膜细胞内化学物质的足够成分和相互作用隔室及其坚固性。 2. 通过耦合随机和连续模型，以离体和体内实验数据为条件，发现足够的分子种类、复合物和隐藏的化学亲和力，以重现活细胞的化学区室化。 3. 扩展多相建模的数值工具，以适应由化学动力学、液滴粘弹性和液-液相分离引起的流动驱动的强烈波动和不平衡行为。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。

项目成果

Linear second order energy stable schemes for phase field crystal growth models with nonlocal constraints

具有非局部约束的相场晶体生长模型的线性二阶能量稳定方案

• DOI: 10.1016/j.camwa.2019.07.030
• 发表时间: 2020-02-01
• 期刊: Comput. Math. Appl.
• 作者: Xiaobo Jing;Qi Wang
• 通讯作者: Qi Wang

Local structure-preserving algorithms for phase field models of graphene growth

石墨烯生长相场模型的局部结构保持算法

• 发表时间: 2022-01
• 期刊: Journal of scientific computing
• 影响因子: 2.5
• 作者: 13. Lin Lu; Qi Wang
• 通讯作者: Qi Wang

Supplementary variable method for structure-preserving approximations to partial differential equations with deduced equations

用推导方程保结构逼近偏微分方程的补充变量法

• DOI: 10.1016/j.aml.2020.106576
• 发表时间: 2020-12-01
• 期刊: Appl. Math. Lett.
• 作者: Qi Hong;Jun Li;Qi Wang
• 通讯作者: Qi Wang

Arbitrarily high-order unconditionally energy stable SAV schemes for gradient flow models

梯度流模型的任意高阶无条件能量稳定SAV方案

• DOI: 10.1016/j.cpc.2019.107033
• 发表时间: 2020-04
• 期刊: Computer Physics Communications
• 影响因子: 6.3
• 作者: Gong, Yuezheng;Zhao, Jia;Wang, Qi
• 通讯作者: Wang, Qi

Arbitrarily High-Order Unconditionally Energy Stable Schemes for Thermodynamically Consistent Gradient Flow Models

热力学一致梯度流模型的任意高阶无条件能量稳定方案

• DOI: 10.1137/18m1213579
• 发表时间: 2020-01
• 期刊: SIAM Journal on Scientific Computing
• 影响因子: 3.1
• 作者: Gong, Yuezheng;Zhao, Jia;Wang, Qi
• 通讯作者: Wang, Qi