Simulating a minimal cell: Integrating experiment and theory

所有细胞都共享一套普遍的、最小的生命必需的生物学过程。对该集合的搜索导致了最小细菌细胞JCVI-syn3A的构建。JCVI-syn3A的基因组长543 kbp，有493个基因，其基因组比自然界中发现的任何独立复制的细胞的基因组都小，形态健壮，可以在无压力的实验室生长介质中每两小时分裂一次。在这个最小的细胞中，几乎所有的基因都是必不可少的，而且细胞足够小，可以通过利用图形处理单元(GPU)计算来尝试在生物相关的长度、时间和浓度范围内对所有细胞功能进行完整的描述。最近在GPU计算和3D成像方面的成功使得现在有可能建立这种最小的细菌细胞的全细胞计算模型，并研究生命的物理规律。在这个项目中，研究人员将研究功能尚未确定的最小细胞中迄今尚未确定的特征基因，并利用这些信息构建包含所有细胞功能的全细胞计算模型。该项目的结果将使研究团队能够预测细胞在各种扰动下的行为，从而解释完整细胞是如何工作的。更广泛的教育影响包括对学生和博士后研究人员的培训，以及通过研讨会和YouTube/VR平台推广到更广泛的社区，促进科学的公开传播。该项目旨在通过多模式实验全面描述最小细菌细胞JCVI-Syn3A的特征，并使用在该项目期间开发的新的模拟方法将不同种类的数据集成到最小细胞的多尺度、可预测的全细胞计算模型中。特别是，主要调查人员提出了两个主要目标。主要目标1：最小细胞及其细胞网络的特征。1A：PI将利用基于CRISPRi的表达调控来探索未知但必要的其余基因的功能(由转座子插入实验确定)，并研究细胞表型的相应变化。1B：研究人员将完善和扩展JCVI-Syn3A的现有代谢模型，方法是开发一种明确的生长介质，作为所有后续实验的基础；研究细胞组成和功能的各个方面；并将稳态代谢模型扩展为动力学模型。1C：将使用冷冻电子断层扫描(CET)获得JCVI-syn3A细胞的可视化蛋白质组学，以提取细胞范围内大分子复合体的丰度和空间分布。主要目标2：研究人员将使用基于GPU的网格微生物软件将异质实验数据集成到全细胞计算模型中，该软件可以处理细胞的空间异质环境。2A：该团队将致力于混合方法的方法学开发，以允许处理浓度范围和动态行为差异很大的物种。为了弥合这些尺度，研究人员将开发随机-确定性混合方法，将反应扩散主方程(RDME)与描述细胞成分的布朗动力学(BD)和常微分方程(ODE)相结合。2B：他们将把代谢网络与核糖体组装、转录、翻译、信使核糖核酸/蛋白质衰退、DNA复制、细胞生长和分裂模型结合起来。2C。使用他们构建的空间分辨模型，研究人员将检验细胞表型对动力学参数分配的敏感性，并通过与不同的生化、遗传和结构实验(如CET)进行比较，在发育的每个阶段验证整个细胞模型。这一奖项反映了NSF的法定使命，并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，被认为值得支持。

All cells share a universal, minimal set of biological processes essential for life. The search for this set led to the construction of the minimal bacterial cell JCVI-syn3A. With 493 genes in a genome of 543 kbp, JCVI-syn3A has a genome smaller than that of any independently-replicating cell found in nature, a robust morphology, and can divide every two hours in a stress-free laboratory growth medium. Nearly all genes in this minimal cell are essential, and the cell is small enough that a complete description of all cellular functions can be attempted over biological relevant length, time, and concentrations scales by exploiting graphics processing unit (GPU) computing. Recent successes in GPU computing, and 3D imaging have made it now possible to build a whole-cell computational model of this minimal bacterial cell and to investigate what are the physical rules of life. In this project the investigators will study hitherto uncharacterized genes in the minimal cell whose functions have not been identified, and use this information to construct a whole-cell computational model encompassing all cellular functions. The outcome of this project will allow the research team to predict cellular behavior under a variety of perturbations, and thus explain how a complete cell works. The educational broader impacts include the training of students and postdoctoral investigators, and outreach to the broader community through workshops and YouTube/VR platforms facilitating the public dissemination of the scienceThis project aims to comprehensively characterize the minimal bacterial cell JCVI-syn3A through multimodal experiments, and to integrate the heterogeneous data into a multi-scale, predictive whole-cell computational model of the minimal cell using novel simulation methods developed during this project. In particular, the principal investigators have proposed two major aims. Major aim 1: Characterization of the minimal cell and its cellular networks. 1a: The PIs will probe the function of the remaining genes of unknown, but essential function (as determined by transposon insertion experiments) using CRISPRi-based expression modulation and study the corresponding change in cellular phenotype. 1b: Investigators will refine and expand the existing metabolic model for JCVI-syn3A by developing a defined growth medium as a basis for all subsequent experiments; studying various aspects of cellular composition and functionality; and expanding the steady-state metabolic model to a kinetic model. 1c: Visual proteomics of JCVI-syn3A cells will be obtained using cryo-electron tomography (CET) to extract cell-wide abundance and spatial distribution of large macromolecular complexes. Major aim 2: Researchers will integrate the heterogeneous experimental data into a whole-cell computational model using the GPU-based Lattice Microbes software that can treat the spatially heterogeneous environment of the cell. 2a: The team will engage in methodological development of hybrid methods that will allow handling of species with vastly different concentration ranges and dynamic behavior. To bridge these scales, investigators will develop hybrid stochastic-deterministic methodologies that couple Reaction Diffusion Master Equations (RDME) with Brownian dynamics (BD) and ordinary differential equation (ODE) descriptions of cellular components. 2b: They will integrate the metabolic network with models of ribosome assembly, transcription, translation, mRNA/protein decay, DNA replication, cell growth and division. 2c. Using their constructed, spatially resolved model the investigators will examine the sensitivity of the cellular phenotype to the assignment of kinetic parameters and validate the whole cell model at each stage of development through comparisons to diverse biochemical, genetic and structural experiments such as CET.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.