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

基本信息

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

项目摘要

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.

真核细胞已经发展出多种机制，用于隔离和维持局部化学或分子浓度。最明显的是一种物理膜，例如将细胞质与周围环境分开的细胞膜或将核定染色体DNA限制在细胞核内的核膜。隔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，否则这些梯度将使细胞含量均匀，并且无法允许对关键细胞过程的空间调节。在细胞生物学上，最近确定和当前的强烈重点是在没有物理膜的情况下形成的化学室。该项目的重点是一个特定的例子：细胞质蛋白和RNA的结合到复合物中，通过液态液相分离（LLP）形成富含蛋白质的液滴。通过汇总数学，计算和生物科学家，研究人员旨在开发一个一般的计算建模平台，以研究来自LLP的细胞质液滴及其空间分布。目的是从机械上理解这些隔室如何在活细胞中的mRNA定位和表达中建立和保留细胞质异质性，以及负责的分子物种，复合物和动力学时间表。通过该平台在其他活细胞中的应用，有可能了解LLP的基本细胞特异性分子成分和化学动力学，从而有助于理解细胞生物学跨生物学的细胞内分室化的多样性。膜研究的细胞生物学及其在建立细胞外和细胞内化学区室中的作用丰富。然而，关于分子蛋白，细胞器和染色体DNA在细胞质内或核内如何在没有物理膜的情况下，在化学相互作用和自我组织中如何进行化学相互作用和自我组织，以化学相互作用和自我组织，以化学相互作用和自我组织，以化学相互作用和自我组织，在细胞质中或核内如何进行化学相互作用和自我组织。该项目的研究人员武装着原代分子和物种复合物的空间和时间实验数据，重点是三个特定目标。 1。一个计算建模平台，旨在探索原代分子（蛋白质，RNA，蛋白-RNA复合物）和微观（核，膜，膜）物种，化学物种亲和力和空间限制条件的输入空间。该平台将产生结果的相图，该结果模仿活细胞数据（复合物和分子物种的动态自组织，由于液 - 液相分离而引起的液滴形成），并且揭示了足够的成分和相互作用的足够的成分和相互作用车厢及其稳健性。 2。通过耦合随机和连续建模，在体内和体内实验数据上进行调节，以发现足够的分子物种，复合物和隐藏的化学亲和力，从而再现了活细胞的化学隔室化。 3。扩展用于多相建模的数值工具，以适应由化学动力学，液滴的粘弹性和液态液相分离引起的强大波动和超平衡行为。这反映了NSF的法定任务，并被认为是值得的，并被认为是值得的。通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。

项目成果

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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
通讯作者：
Holly A. Schroeder;J. Newby;Alison Schaefer;Babu Subramani;Alan L. Tubbs;M. Gregory Forest;Edward A. Miao;S. Lai

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

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