Researchers use quantum computing to predict relationships between genes

The quantum classical framework using the qscGRN model to infer the corresponding biological GRN. Credit: npj Quantum information (2023). DOI: 10.1038/s41534-023-00740-6

In a new multidisciplinary study, Texas A&M University researchers have shown how quantum computing, a new type of computing capable of processing additional types of data, can aid genetic research and used it to discover new links between genes that scientists were previously unable to detect. .

Their project used new computing technology to map gene regulatory networks (GRNs), which provide information about how genes can turn each other on or off.

As the team published in npj Quantum informationQuantum 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 influence each other,” Cai said. “For example, if one gene turns on or off, it can then change another, which could ultimately change three, five or 20 others.”

“As our quantum computing GRNs are built in a way that allows us to capture more complex relationships between genes than traditional computing, we have discovered connections between genes that people were not previously aware of,” he said. he declared. “Some researchers specializing in the type of cells we studied read our paper and realized that our predictions using quantum computing matched their expectations better than the traditional 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 helpful ones.

“If you can predict gene expression via GRN and understand how these changes translate into cell state, you may be able to control certain outcomes,” Cai said. “For example, changing the way a gene is expressed could end up inhibiting the growth of cancer cells.”

Making the most of new technology

Using quantum computing, Cai and his team are overcoming the limitations of older computing technologies used to map GRNs.

“Before using quantum computing, algorithms could only compare two genes at a time,” Cai said.

Cai explained that only comparing genes in pairs could lead to misleading conclusions, because genes can operate in more complex relationships. For example, if gene A turns on and so does gene B, that doesn’t always mean that gene A is responsible for changing gene B. In fact, it could be that gene C is changing both genes.

“With traditional computing, data is processed in bits, which have only two states: on and off, or 1 and 0,” Cai explained. “But with quantum computing, you can have a state called superposition that is both on and off simultaneously. This gives us a new type of bit: the quantum bit, or qubit.

“Through superposition, I can simulate both the active and inactive states of a GRN gene, as well as the impact of that single gene on other genes,” he said. “You get a more complete picture of how genes influence each other.”

Take the next step

Although Cai and his team have worked hard to show that quantum computing is useful for the biomedical field, there is still much work to be done.

“It’s a very new field,” Cai said. “Most people working in quantum computing have a physics background. And biology people generally don’t understand how quantum computing works. You really have to be able to understand both sides.”

That’s why the research team includes both biomedical scientists and engineers like Dr. Cai. student Cristhian Roman Vicarra, who is a key member of the research team and led the study behind the recent publication.

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

For now, it is important to understand as clearly as possible how healthy cells work before comparing them to mutated or diseased cells.

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

More information:
Cristhian Roman-Vicharra et al, Quantum gene regulatory networks, npj Quantum information (2023). DOI: 10.1038/s41534-023-00740-6

Provided by Texas A&M University

Quote: Researchers use quantum computing to predict genetic relationships (November 20, 2023) retrieved November 20, 2023 from

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