
NSF Org: |
MCB Division of Molecular and Cellular Biosciences |
Recipient: |
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Initial Amendment Date: | August 10, 2018 |
Latest Amendment Date: | August 27, 2018 |
Award Number: | 1818344 |
Award Instrument: | Standard Grant |
Program Manager: |
David Rockcliffe
drockcli@nsf.gov (703)292-7123 MCB Division of Molecular and Cellular Biosciences BIO Directorate for Biological Sciences |
Start Date: | August 15, 2018 |
End Date: | July 31, 2024 (Estimated) |
Total Intended Award Amount: | $1,500,000.00 |
Total Awarded Amount to Date: | $1,500,000.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
506 S WRIGHT ST URBANA IL US 61801-3620 (217)333-2187 |
Sponsor Congressional District: |
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Primary Place of Performance: |
IL US 61820-7473 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | Systems and Synthetic Biology |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.074 |
ABSTRACT
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 science
This 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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Our team constructed a bacterium, JCVI-Syn3A, with a minimal genome by systematically removing genes from the bacterium Mycoplasma mycoides. The remaining 493 genes are essential for the organism to grow rapidly in a stress-free laboratory environment. Simulating the minimal cell has expanded our understanding of the first principles of cell biology through the integration of 4D (space plus time) GPU-based computational modeling techniques and data from cryo-electron tomography, DNA contact maps, genome-wide proteomics, essentiality measurements and RNA/DNA sequencing, fluorescent microscopy, and Bayesian estimations of kinetic parameters for its essential metabolic reactions. Genome-scale simulations of the minimal cell must handle multiple concentration scales when all cellular components are considered, ranging from nanomolar (genes) to millimolar (metabolites). To bridge these scales, we developed hybrid stochastic-deterministic methods that use reaction-diffusion master equations (RDME) and chemical master equations to capture fluctuations in the genetic information processes coupled with ordinary differential equations to model the essential metabolic reactions. Determining the frequency at which information is exchanged between the various methods was a major challenge in developing our Lattice Microbes (LM) cell simulation software and analysis programs.
Research results from this funding are presented in 18 publications. Kinetic models for each cellular subsystem were systemically developed and validated before combining the metabolic and genetic information processes into a whole cell model. Fundamental behaviors emerged from the simulations of this living minimal cell reported in Cell 2022. Time-dependent behaviors of concentrations and reaction fluxes from stochastic-deterministic simulations over a cell cycle reveal how the cell balances demand of its metabolism, genetic information processes, and growth, and offer insight into the minimal principles of life. The energy economy of each process including active transport of amino acids, nucleosides, and ions were analyzed. The whole cell model revealed how emergent imbalances could lead to slowdowns in the rates of transcription and translation, and in some cases to cell death as the minimal cell has little remaining regulation. Integration of experimental data is critical to build a kinetic model from which emerges a distribution of mRNA half-lives for its 452 protein coding genes, multiple DNA replication events, and the experimentally observed doubling behavior. From the first 20 minutes of the 110-minute doubling time, probabilistic factors were estimated allowing the simulation of the entire cell cycle. These simulations created predictions about the nature and types of DNA theta structures and conditions that could lead to cell death. Coverage and sequence depth from DNA sequencing results recently obtained in this funding period are being used to validate the kinetic mechanism of DNA replication being applied. Trajectories of both healthy and dying cells have motivated new applications of machine learning to analyze all the time-dependent data from the LM simulations and categorize the critical cell states at the decisive moments in its cell cycle.
A research team of collaborators from University of Illinois at Urbana-Champaign, J. Craig Venter Institute, University of California at San Diego, and the Technical Universities at Leiden and Dresden were needed to acquire all the necessary multimodal and heterogeneous data for constructing the computational model and validating the simulations. The Lattice Microbes program is available for other researchers and are part of the public GitHub accompanying published articles. Ongoing improvements are being made to extend its performance efficiency so longer times and more detailed scenarios for the assembly of large protein complexes can be simulated. The whole cell model for the minimal cell is consistently being improved as more experimental information becomes available that reveal additional mechanistic details of its fundamental biological processes. Because JCVI-syn3A is a minimal cell, it will continue to play an important role in attempts to build a synthetic cell.
PI Luthey-Schulten is now director of the NSF Science and Technology Center for Quantitative Cell Biology where its faculty have developed a pipeline to convert particle models from LM and images from 3D microscopes into cell models that can be downloaded into Minecraft videos– providing an exciting new modern educational tool for teaching cell biology that is accessible to the broad public. Lessons learned from the research to develop 4D models of the minimal cell are now being applied to studies of eukaryotic cells and suggest new 4D experiments needed to measure cellular dynamics. Two STC faculty from U. Stockholm and U. Groningen are using LM models to build coarse-grained atomistic models that are simulated using the Martini force field and Gromacs molecular dynamics program. Such physics-based simulations will serve to validate predictions from the ultra-coarse-grained LM simulation and improve the cell states used to initialize the simulation.
Last Modified: 11/29/2024
Modified by: Zaida A Luthey-Schulten
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