Cracking the second code of Life using AI
Solving the mystery behind reading and writing DNA enhancer sequences
Until now, it was virtually impossible to predict enhancer activity based on sequence alone. Scientists from the lab of Alexander Stark at the IMP now changed this by developing a machine learning algorithm trained on DNA accessibility and expression data from fruit fly embryos. This neural network can not only accurately predict enhancer activity, but is also able to write synthetic enhancers that direct gene expression in specific tissues at specific stages.
For Alexander stark the fundamental insight into Life is the most important aspect of this study, which as he says marks the peak of his scientific career to date. In addition, their model is a true game-changer for synthetic biology & gene therapy – fields in which the ability to precisely manipulate gene expression patterns is invaluable. Curious to learn more? Check out their amazing paper and the cover I designed for their discovery!
