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Modeling the cell-by-cell spread of respiratory infections

An important issue with SARS-CoV-2 infection is why viral load and patient outcomes vary greatly among individuals. Since it is difficult to see how the virus spreads to the lungs of infected people, researchers have developed SIMCoV, a computational model that simulates hundreds of millions of cells, including lung cells and immune cells. Credits: Steve Hofmeyr / Berkeley Lab

Steve Hoffmeier, a computational research scientist at Lawrence Berkeley National Laboratory, and a team at the University of New Mexico and Arizona State University have found that COVID-19 and other viral infections spread to the lungs and are transmitted cell-by-cell. I succeeded in modeling the situation. It has never been captured before, so we will process it.


Their new calculation modelThe Coronavirus Spatial Immunity Model (SIMCoV) was developed using the Cori supercomputer at the National Energy Research and Science Computing Center (NERSC), a US Department of Energy user facility at the Berkeley Institute. SIMCoV provides some 3D simulations. lung And how Virus infection Can spread with Cell level.. Specifically, a method by which T cells (B cells or antibodies fight the virus between cells and at the same time fight the virus inside the cell) interact with viral load and other variables to influence the progression of viral load. Is shown. infection.. This model deals with billions of epithelial cells that line the inside of the lung and hundreds of millions of T cells that protect them as individual agents. This is the level of detail made possible by using critical computing resources.

“I feel that this model has a lot of potential to explain different aspects. [COVID-19], And the computational modeling in this case is very powerful. Because it is very difficult to image and understand what is happening at this level of the immune system’s response. PLOS Computational Biology During December. “It’s not even easy to measure the level of T cells. You can measure the level of T cells in the blood, but it doesn’t necessarily indicate the number of T cells that have entered the lungs themselves to fight the infection. No. This model has a lot of room to understand it. “

Earlier models based on differential equations describe the lungs as uniform and assume that the growth of viral infections is also evenly distributed in the lungs. This new cell-by-cell model provides much more spatial detail, that infection is active in some areas and may be dormant in others, and that the virus and immune system interact with each other. It shows that different areas can flare and then recede over time as they act.

According to Hofmeyr, this model also takes into account the spatial distribution of the virus. “For example, we conducted experiments showing that there was a certain number of virions or encapsulated viruses at the time of initial infection,” he said. “Compared to collecting the same number and distributing it to the lungs, collecting a fixed number and concentrating it in one place makes the second case much worse. The reason for the worse is the immune system. Is much worse. You need to find different infections and you need to disperse T cells to find them in different places. This is a much more difficult problem. “

Hofmeyr and his colleagues developed the model using UPC ++. It is a distributed for C ++ developed by Berkeley Lab’s Pagoda Project, an effort to research and develop cutting-edge software for implementing high-performance applications and software infrastructure, funded by the Exascale Computing Project. Memory library. Partitioned global address model. UPC ++ supports physically distributed global memory, allowing it to be effectively extended to a large number of processors, including future SIMCoV models of the entire lung.

“I’ve been using UPC ++ a lot lately and found it to be a great programming model. It’s done very well and it’s very easy to parallelize things,” Hoffmeyer said. Told. “It has some really powerful features.”

One of the possible extensions of SIMCoV is to model the mechanism by which the virus actually spreads deep into the lungs. This is whether the air circulation spreads from one part of the leaf to another or repeatedly to the bottom with each inhalation and exhalation. Lungs due to infection of the upper respiratory tract. The same model may also help to understand the mechanism behind “long COVID” in which a person infected with COVID continues to experience symptoms for longer than usual.

The SIMCoV model was developed in the context of COVID-19, but the findings are also applicable to other viral lung infections. Hoffmeyer hopes that this model may apply to influenza in the future. Influenza kills hundreds of thousands of people around the world each year. This model may also be useful in running studies on interventions for viral lung infections. Scientists may be able to identify potential treatment strategies by experimenting with different interventions and their effects in the progression of the disease at different times.

Hofmeyr compares the size and power of current formats of SIMCoV models with those normally applied in small human situations. “These powerful models are similar to the types of modeling we do in physical and chemical systems,” he says. “If the experiment is too big to actually do the experiment you really need to do because you don’t know how to answer all the questions. You can’t measure everything. Maybe you’re doing it. Hmm. Experiments with fusion science or something. This kind of model provides a similar kind of support. ”


Respiratory model of COVID-19 made from patient-derived stem cells


For more information:
Melanie E. Moses et al, a spatially dispersed infection, increases viral load in a computational model of SARS-CoV-2 lung infection. PLOS Computational Biology (2021). DOI: 10.1371 / journal.pcbi.1009735

Quote: Modeling of cell-by-cell spread of respiratory infections (February 10, 2022) from https: //phys.org/news/2022-02-cell-by-cell-respiratory-infections.html 2022 Obtained on February 10th.

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Modeling the cell-by-cell spread of respiratory infections

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