Researchers have simulated a living cell in 3D thanks to the power of GPUs

Everything is fine living cell has a “microcosm“composed of thousands of components responsible for energy production, building proteins, transcribing genes and more. Understand how all components interact and change in response to internal and external signals it helps to better understand the fundamental principles of lifethis is why the simulation carried out by a team from the University of Illinois at Urbana-Champaign based on GPUs, in this case those from NVIDIA, is important.

The group of scientists is succeeded recreate a 3D simulation that scales the physical and chemical characteristics of a “minimal” living cell, developing a fully dynamic model that mimics its behavior. The study was published in the journal Cell.

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The minimum cell contains a small set of genes essential for survival, function and replication. These cells are simpler than natural cells, making them easier to recreate digitally. “Even a minimal cell requires 2 billion atoms,” said Zaida Luthey-Schulten, professor of chemistry and co-director of the university’s Center for Living Cell Physics. “You can’t make a 3D model like this in a realistic human timescale without a GPU“.

The model is based on the use of NVIDIA GPUs for simulate 7000 genetic information processes in a 20 minute cell cycle, making it what scientists consider to be the longest and most complex cell simulation to date. Once tested and refined, whole-cell models could help scientists predict how changes in real-world conditions or cell genomes will affect their function.

To build the living cell model, the Illinois researchers simulated one of the simplest living cells, a parasitic bacterium called mycoplasma. They based the model on a small version of a mycoplasma cell synthesized by scientists at the J. Craig Venter Institute in La Jolla, California, which was just under 500 genes to keep it viable. For comparison, a single cell of E. coli contains about 5,000 genes. A human cell has more than 20,000.

The Luthy-Schulten team then used the known properties of the inner workings of mycoplasma, including amino acids, nucleotides, lipids and small molecule metabolites, to build the model with DNA, RNA , proteins and membranes. “We had enough feedback to be able to replicate everything we knew,” he said.

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Snapshot of the 20 minute 3D space simulation, showing yellow and purple ribosomes, red and blue degradosomes and smaller spheres representing DNA polymers and proteins.

Thanks to the Lattice Microbes software capable of taking advantage of the Tensor cores of NVIDIA GPUs, the researchers performed a 20-minute 3D simulation of the cell’s life cycle, before it begins to greatly expand or replicate its DNA. The model showed that the cell devotes most of its energy to transporting molecules across the cell membranewhich fits the profile of a parasitic cell that obtains most of what it needs to survive from other organisms.

If you did these calculations in series, or at the level of all atoms, it would take years“, said Zane Thornburg, lead author of the study. “But since these are all independent processes, we can integrate parallelization into the code and use GPUs.”

The simulations also allowed Thornburg to calculate the natural lifespan of messenger RNAs, the genetic blueprints for building proteins. They also revealed a relationship between the rate at which membrane lipids and proteins were synthesized and changes in membrane surface area and cell volume.

“We simulated all the chemical reactions within a minimal cell, from its birth to the time it divides two hours later,” Thornburg said. “From there, we get a model that tells us how the cell behaves and how we can make it more complex to change its behavior,” said Professor Luthey-Schulten.

Thornburg is working on another GPU-accelerated project to simulate cell growth and division in 3D. “The team recently adopted NVIDIA DGX systems and RTX A5000 GPUs to speed up their work,” NVIDIA points out, “noting that using the A5000 GPUs improved simulation time by 40% compared to a workstation with a previous generation NVIDIA GPU”.