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: 1816630
• 负责人: M Forest
• 金额: $ 24.97万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Continuing 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=1816630&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。区室化的机制是必不可少的，因为它们覆盖了浓度梯度的扩散平滑，否则会使细胞内容物均匀化，并且不能允许关键细胞过程的空间调节。细胞生物学中最近确定的和当前的重点是在没有物理膜的情况下形成的化学隔室。 该项目侧重于一个具体的例子：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，研究人员的目标是开发一个通用的计算建模平台，以研究LLPS产生的细胞质液滴及其空间分布。 其目的是了解机械这些隔间如何建立和保存在活细胞中的mRNA定位和表达的细胞质异质性，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，从而有助于了解细胞生物学中细胞内区室化的多样性。在细胞生物学中，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质、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产生、维持和进化局部化学和大分子隔室，人们知之甚少。本项目的研究人员利用初级分子物种和物种复合物的空间和时间实验数据，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，化学物种亲和力和空间限制条件的输入空间。 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，由于液-液相分离而形成的液滴），并揭示无膜细胞内化学隔室的足够成分和相互作用及其鲁棒性。2. 通过耦合随机和连续建模，调节离体和体内实验数据，以发现足够的分子种类，复合物和隐藏的化学亲和力，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-液相分离引起的诱导流驱动的强烈波动和失衡行为。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

Observation of transition cascades in sheared liquid crystalline polymers

剪切液晶聚合物中转变级联的观察

• DOI: 10.1039/d0sm00275e
• 发表时间: 2020
• 期刊: Soft Matter
• 影响因子: 3.4
• 作者: Fox, Ryan J.;Forest, M. Gregory;Picken, Stephen J.;Dingemans, Theo J.
• 通讯作者: Dingemans, Theo J.

Three-Dimensional Thermodynamic Simulation of Condensin as a DNA-Based Translocase

• DOI: 10.1007/978-1-4939-9520-2_21
• 发表时间: 2019-01-01
• 期刊: SMC COMPLEXES: METHODS AND PROTOCOLS
• 作者: Lawrimore, Josh;He, Yunyan;Bloom, Kerry
• 通讯作者: Bloom, Kerry

LPS-binding IgG arrests actively motile Salmonella Typhimurium in gastrointestinal mucus

• DOI: 10.1038/s41385-020-0267-9
• 发表时间: 2020-03
• 期刊: Mucosal Immunology
• 影响因子: 8
• 作者: Holly A. Schroeder;J. Newby;Alison Schaefer;Babu Subramani;Alan L. Tubbs;M. Gregory Forest;Edward A. Miao;S. Lai

Modeling the Mechanisms by Which Coexisting Biomolecular RNA–Protein Condensates Form

共存生物分子 RNA-蛋白质凝聚物形成机制的建模

• DOI: 10.1007/s11538-020-00823-x
• 发表时间: 2020
• 期刊: Bulletin of Mathematical Biology
• 影响因子: 3.5
• 作者: Gasior, K.;Forest, M. G.;Gladfelter, A. S.;Newby, J. M.
• 通讯作者: Newby, J. M.

Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking.

基于机器学习的 3D 粒子跟踪揭示了细胞质的空间异质性。

• DOI: 10.1091/mbc.e20-03-0210
• 发表时间: 2020
• 期刊: Molecular biology of the cell
• 影响因子: 3.3
• 作者: McLaughlin,GraceA;Langdon,ErinM;Crutchley,JohnM;Holt,LiamJ;Forest,MGregory;Newby,JayM;Gladfelter,AmyS
• 通讯作者: Gladfelter,AmyS