Physorg March 5, 2018
An international team of researchers (USA – UC San Diego, Israel) developed what they call a “visible” neural network and used it to build DCell, a model of a functioning brewer’s yeast cell, commonly used as a model in basic research. To do this, they amassed all knowledge of cell biology in one place and created a hierarchy of these cellular components. Then they mapped standard machine learning algorithms to this knowledge base. “Learning” is guided only by real-world cellular behaviors and constraints coded from approximately 2,500 known cellular components. The team inputs information about genes and genetic mutation and DCell predicts cellular behaviors, such as growth. They trained DCell on several million genotypes and found that the virtual cell could simulate cellular growth nearly as accurately a real cell grown in a laboratory… read more. TECHNICAL ARTICLE
Screenshot from d-cell.ucsd.edu, where researchers can use DCell, a new virtual yeast cell developed at UC San Diego School of Medicine. Credit: UC San Diego Health