Trey Ideker (University of California, San Diego)
Although artificial neural networks capture a variety of human functions, their internal structures are hard to interpret. In the life sciences, extensive knowledge of cell biology provides an opportunity to design 'visible' neural networks (VNNs) which couple the model's inner workings to those of real systems. Here we develop D-Cell, a VNN embedded in the hierarchical structure of 2526 subsystems comprising a eukaryotic cell. Trained on several million genotypes, D-Cell simulates cellular growth nearly as accurately as laboratory observations. During simulation, genotypes induce patterns of subsystem activities, enabling in-silico investigations of the molecular mechanisms underlying each genotype-phenotype association.
