Researchers use quantum computing to predict genetic relationships

Researchers use quantum computing to predict genetic relationships

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Classical quantitative framework using the qscGRN model to infer the corresponding biological GRN. credit: Quantum Information npj (2023). doi: 10.1038/s41534-023-00740-6

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Classical quantitative framework using the qscGRN model to infer the corresponding biological GRN. credit: Quantum Information npj (2023). doi: 10.1038/s41534-023-00740-6

In a new interdisciplinary study, researchers at Texas A&M University show how quantum computing — a new type of computing that can process additional types of data — can aid genetic research and be used to discover new connections between genes that scientists previously could not discover. .

Their project used new computing technology to map gene regulatory networks (GRNs), which provide information about how genes can cause each other to be activated or deactivated.

The team also published in Quantum Information npjQuantum computing will help scientists more accurately predict relationships between genes, which could have huge implications for animal and human medicine.

“The GRN is like a map that tells us how genes affect each other,” Kay said. “For example, if one gene is turned on or off, it may change another gene that could change three, five, or 20 other genes in the future.”

“Because the GRNs of quantum computing are built in ways that allow us to capture more complex relationships between genes than in classical computing, we found some connections between genes that people didn’t know about before,” he said. “Some researchers who specialize in the type of cells we studied read our paper and realized that our predictions using quantum computing fit their predictions better than the classical model.”

The ability to know which genes will affect other genes is crucial for scientists looking for ways to stop harmful cellular processes or promote beneficial ones.

“If you can predict gene expression through the GRN and understand how these changes translate to the state of the cells, you may be able to control certain outcomes,” Kay said. “For example, changing how a single gene is expressed can ultimately inhibit the growth of cancer cells.”

Make the most of new technology

Using quantum computing, Cai and his team overcome the limitations of legacy computing techniques used to map GRNs.

“Before quantum computing, algorithms were only able to handle comparing two genes at a time,” Cai said.

Cai explained that comparing genes only in pairs can lead to misleading conclusions, because genes may operate in more complex relationships. For example, if gene A is activated as well as gene B, it does not always mean that gene A is responsible for changing gene B. In fact, it could be gene C that changed both genes.

“With traditional computing, data is processed in bits, which have only two states — on and off, or 1 and 0,” Kay said. “But with quantum computing, you can have a state called superposition that is on and off at the same time. This gives us a new type of bit — a quantum bit, or qubit.”

“Because of the superposition, I can simulate both the active and inactive states of a gene in the GRN, as well as the effect of that single gene on other genes,” he said. “You end up with a more complete picture of how genes influence each other.”

Take the next step

While Cai and his team have worked hard to show that quantum computing is useful in biomedicine, there is still a lot of work to be done.

“It’s a very new field,” Kay said. “Most people who work in quantum computing have a physics background. People on the biological side typically don’t understand how quantum computing works. You really have to be able to understand both sides.”

That’s why the research team includes biomedical scientists and engineers like Kay, Ph.D. student Christian Roman Vichara, a key member of the research team who led the study behind the latest publication.

“In the future, we plan to compare healthy cells with cells with diseases or mutations,” Cai said. “We hope to see how the mutation might affect gene states, expressions, frequencies, etc.”

Currently, it is important to get as clear an understanding as possible of how healthy cells function before comparing them to transformed or diseased cells.

“The first step was to predict this basic model and see if the network we drew made sense,” Kay said. “Now, we can continue from there.”

more information:
Christian Roman Vichara et al., Quantum Gene Regulatory Networks, Quantum Information npj (2023). doi: 10.1038/s41534-023-00740-6

Magazine information:
Quantum Information npj

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