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Living Cell Simulation Created by Researchers Using GPU Acceleration

Researchers at the University of Illinois at Urbana-Champaign have created a unique living cell simulation using NVIDIA graphics card-accelerated software to simulate 2 billion people who metabolize and grow like a living cell. The living cell simulation contains its own microcosm of thousands of components responsible for building proteins, transcribing genes, producing energy, and more. 3D modeling reproduces physical and chemical characteristics at the particle scale and provides researchers with a fully dynamic model that mimics the behavior of a living cell.

Simulation of a living cell

“To build a model of a living cell, the Illinois researchers modeled one of the simplest living cells, a parasitic bacterium called mycoplasma. They based the model on a stripped-down version of the mycoplasma cell synthesized by scientists at the J. Craig Venter Institute in La Jolla, California, which had just under 500 genes to keep it alive. For comparison, one E. coli cell has about 5,000 genes. There are over 20,000 of them in a human cell.

The Lüthi-Schulten team then used known properties of the mycoplasma’s inner workings, including amino acids, nucleotides, lipids, and small molecule metabolites, to build a model with DNA, RNA, proteins, and membranes. Even the smallest cell requires 2 billion atoms,” said Zaida Lutei-Schulten, professor of chemistry and co-director of the university’s Center for Living Cell Physics. “You can’t create a 3D model like this in realistic human timescale without GPUs.”

“Using Lattice Microbes software on NVIDIA Tensor Core GPUs, the researchers ran a 20-minute 3D simulation of the life cycle of a cell before it begins to significantly expand or replicate its DNA. The model showed that the cell spends most of its energy transporting molecules across the cell membrane, consistent with its parasitic cell profile. “If you were to do these calculations sequentially or at the level of the entire atom, it would take years,” said graduate student and lead author of the paper Zein Thornburgh. “But since they are all independent processes, we could implement code parallelization and use GPUs.”

NVIDIA: JC

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