Researchers are interested in genetically modifying trees for a variety of applications, from biofuels to paper production. They also want to steer clear of modifications with unintended consequences. These consequences can arise when intended modifications to one gene results in unexpected changes to other genes. A new model aims to predict these changes, helping avoid unintended consequences, and paving the way for more efficient research in the fields of genetic modification and forestry.
A model for better predicting the unpredictable byproducts of genetic modification
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