Simulating a minimal cell: Integrating experiment and theory

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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.