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University of Cambridge
A new computer model that simulates blood cell development could aid the discovery of novel treatments for hematologic malignancies, according to researchers.
“With this new computer model, we can carry out simulated experiments in seconds that would take many weeks to perform in the laboratory, dramatically speeding up research into blood development and the genetic mutations that cause leukemia,” said Bertie Gottgens, PhD, of the University of Cambridge in the UK.
Dr Gottgens and his colleagues explained this research in Nature Biotechnology.
To start, the researchers measured the activity of 48 genes in 3934 hematopoietic progenitor cells. They used the resulting dataset to construct the computer model of blood cell development, using computational approaches originally developed at Microsoft Research for the synthesis of computer code.
Subsequent lab experiments validated the accuracy of the model.
The researchers noted that the model can be used to simulate the activity of key genes implicated in hematologic malignancies. For example, around 1 in 5 children who develop leukemia have a faulty version of the RUNX1 gene, as do a similar proportion of adults with acute myeloid leukemia.
The computer model shows how RUNX1 interacts with other genes to control blood cell development. The gene produces the RUNX1 protein, which, in healthy patients, activates a network of key genes. In leukemia patients, an altered form of the protein is thought to suppress the network.
If the researchers change the “rules” in the network model, they can simulate the formation of abnormal leukemia cells. By tweaking the leukemia model until the behavior of the network reverts back to normal, the team can identify pathways that, potentially, could be targeted with drugs.
“Because the computer simulations are very fast, we can quickly screen through lots of possibilities to pick the most promising ones as pathways for drug development,” Dr Gottgens said.
“The cost of developing a new drug is enormous, and much of this cost comes from new candidate drugs failing late in the drug development process. Our model could significantly reduce the risk of failure, with the potential to make drug discovery faster and cheaper.”
