Simulating a minimal cell: Integrating experiment and theory

所有的细胞都有一套对生命至关重要的普遍的、最小的生物学过程。对这组的搜索导致了最小细菌细胞JCVI-syn3A的构建。在543 kbp的基因组中有493个基因，JCVI-syn3A的基因组比自然界中发现的任何独立复制细胞的基因组都要小，形态健壮，并且可以在无压力的实验室生长培养基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，并且细胞足够小，可以通过利用图形处理单元（GPU）计算在生物相关的长度、时间和浓度尺度上尝试对所有细胞功能进行完整描述。 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细菌细胞的全细胞计算模型，并研究生命的物理规则。 在这个项目中，研究人员将研究迄今为止尚未确定其功能的最小细胞中的未知基因，并使用这些信息构建一个包含所有细胞功能的全细胞计算模型。 该项目的成果将使研究团队能够预测各种扰动下的细胞行为，从而解释完整的细胞如何工作。 更广泛的教育影响包括培训学生和博士后研究人员，并通过研讨会和YouTube/VR平台向更广泛的社区推广，促进科学的公开传播该项目旨在通过多模式实验全面表征最小细菌细胞JCVI-syn3A，并将异质数据整合到多尺度，预测性的全细胞计算模型的最小细胞使用新的模拟方法在这个项目中开发的。特别是，主要研究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表征。1a：PI将使用基于CRISPRi的表达调节来探测其余未知但必需功能的基因的功能（如通过转座子插入实验所确定的），并研究细胞表型的相应变化。1b：研究人员将通过开发一种确定的生长培养基作为所有后续实验的基础来完善和扩展JCVI-syn3A的现有代谢模型;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细胞的视觉蛋白质组学，以提取大分子复合物的细胞范围丰度和空间分布。主要目标二：研究人员将使用基于GPU的Lattice Microbes软件将异质实验数据整合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：该团队将参与混合方法的方法学开发，该方法将允许处理具有截然不同的浓度范围和动态行为的物种。为了弥合这些规模，研究人员将开发混合随机-确定性方法，将反应扩散主方程（RDME）与布朗动力学（BD）和细胞组分的常微分方程（ODE）描述相结合。2b：他们将把代谢网络与核糖体组装、转录、翻译、mRNA/蛋白质衰变、DNA复制、细胞生长和分裂的模型结合起来。2c.使用他们构建的空间分辨模型，研究人员将检查细胞表型对动力学参数分配的敏感性，并通过与不同生化，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

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.