Quantum Computing Used to Predict Gene Relationships

Genetic research scientists at Texas A&M University have used quantum computing to predict previously undiscovered links between genes. The method could offer insights into animal and human medicine including helping find new ways to stop the growth of cancer cells.

They used computing technology to map gene regulatory networks (GRNs), which show how genes can cause each other to activate or deactivate. If one gene is switched on or off, it could affect a number of other genes further down the line.

Insight into the way genes interact could provide new therapies that involve switching off or triggering genes, including slowing the growth of cancer cells.  

Quantum computing enabled the researchers to investigate more quantum networks that has been possible using classical computing and discover new links between genes and the results were shown to better match predicted behaviours.

Previous classical methods had only been able to compare two genes at a time. This means it could miss steps in a sequence and incorrectly link the effect of activating one gene to the results observed in another gene.

Quantum computing uses the property of superposition, where a qubit can exist in a state between on and off, which means it can more readily simulate interacting genes in active and inactive states.

Related:Quantum-Enabled Digital Twin Boosts Green Hydrogen Production

The team plans to use the new quantum computing modelling method to understand more about how healthy cells work to create a baseline model. They then plan to compare healthy cells to ones with disease or mutations.

This article first appeared in IoT World Today's sister site, Enter Quantum.