Simulation of a Living Cell

Researchers create living cell simulations using NVIDIA GPUs.

Researchers from the University of Illinois at Urbana-Champaign developed GPU-accelerated software to simulate a 2-billion-atom cell that metabolizes and grows like a living cell.

Every live cell is a bustling microcosm with thousands of components that are responsible for energy production, protein synthesis, gene transcription, and other functions.

Scientists at the University of Illinois at Urbana-Champaign have created a completely dynamic model that mimics the activity of a living cell by simulating these physical and chemical features at the particle scale.

The experiment, which was published in the journal Cell, models a living minimal cell with a reduced set of genes required for survival, function, and reproduction. The model uses NVIDIA GPUs to replicate 7,000 genetic information processes throughout the course of a 20-minute cell cycle, making it the longest and most sophisticated cell simulation to date, according to the researchers.

Minimal cells are simpler than actual cells, making them easier to digitally replicate.

“Even a minimal cell requires 2 billion atoms,” said Zaida Luthey-Schulten, chemistry professor and co-director of the university’s Center for the Physics of Living Cells. “You cannot do a 3D model like this in a realistic human time scale without GPUs.”

Whole-cell models, once further validated and refined, can aid scientists in predicting how changes in the environment or genomes of real-world cells would alter their function. Even at this stage, however, modest cell simulation can provide scientists with insight into the physical and chemical processes that underpin living cells.

“What we found is that fundamental behaviors emerge from the simulated cell — not because we programmed them in, but because we had the kinetic parameters and lipid mechanisms correct in our model,” she said.

Simulation of a Living Cell
(A) Ribosome coordinates and cell boundaries are obtained from cryo-electron tomograms. (B) The self-avoiding lattice DNA (red, white, and blue spheres) is folded around the ribosomes (yellow spheres). (C) The membrane (green cubes) surrounds the ribosomes, DNA, and 200-nm radius cytoplasmic space. (D) A representative set of membrane complexes and proteins (degradosomes—red spheres, SecY—blue spheres, and PtsG—green spheres) are randomly distributed in the peripheral membrane and transmembrane space. (E) All other macromolecules are randomly distributed throughout the cytoplasm as shown in all gray spheres. (F) Some rates have been reported from single-molecule experiments such as the DnaA filament formation rate. (G) Otherwise, we used the BRENDA and other databases for kinetic rates. (H) The defined medium composition is used to determine nutrient uptake in our simulations. (I) Spatial simulations require GPU acceleration. (J–L) The spatial simulations predict numbers of (J) active degradosomes breaking down mRNA, (K) transcribing RNAP, and (L) translating ribosomes. (M) The length-dependent kinetics of mRNA decay and requiring mRNA to diffuse to degradosomes results in a distribution of mRNA half-lives. (N) The average number of times each gene is transcribed over the course of the 20-min spatial simulations. (O) A distribution of the average number of times each mRNA type is translated in its lifetime shows that every mRNA is translated at least once in its lifetime on average. 3D visualization was done with VMD (Humphrey et al., 1996).

Lattice Microbes, the GPU-accelerated software co-developed by Luthey-Schulten and used to simulate the 3D minimal cell, is available on the NVIDIA NGC software hub.

The Illinois researchers created the living cell model by simulating one of the simplest living cells, mycoplasma, a parasitic microbe. They built the model on a trimmed-down version of a live mycoplasma cell created by scientists at the J. Craig Venter Institute in La Jolla, Calif.

A single E. coli cell, by comparison, has roughly 5,000 genes. There are almost 20,000 in a single human cell.

Luthy-team Schulten’s then built out the model with DNA, RNA, proteins, and membranes using known aspects of mycoplasma’s inner workings, such as amino acids, nucleotides, lipids, and small molecule metabolites.

“We had enough of the reactions that we could reproduce everything known,” she said.

The researchers did a 20-minute 3D simulation of the cell’s life cycle before it begins to significantly enlarge or copy its DNA, using Lattice Microbes software on NVIDIA Tensor Core GPUs. The model revealed that the parasite cell spent the majority of its energy transferring chemicals across the cell membrane, which fits its parasitic profile.

“If you did these calculations serially, or at an all-atom level, it’d take years,” said graduate student and paper lead author Zane Thornburg. “But because they’re all independent processes, we could bring parallelization into the code and make use of GPUs.”

Thornburg is now working on a GPU-accelerated project that will replicate 3D growth and cell division. To speed up their work, the team recently switched to NVIDIA DGX systems using RTX A5000 GPUs, and discovered that employing A5000 GPUs sped up benchmark simulation time by 40% when compared to a development workstation with a previous-generation NVIDIA GPU.


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