Machine learning predicts behavior of biological circuits: Neural networks cut modeling times of complex biological circuits to enable new insights into their inner workings

In the new study, the researchers trained a neural network to predict the circular patterns that would be created by a biological circuit embedded into a bacterial culture. The system worked 30,000 times faster than the existing computational model. To further improve accuracy, the team devised a method for retraining the machine learning model multiple times to compare their answers. Then they used it to solve a second biological system that is computationally demanding in a different way, showing the algorithm can work for disparate challenges. The results appear online on September 25 in the journal Nature Communications.