Abstract: Examples described herein provide a computer-implemented method for training a neural network using forward signal propagation learning. The method includes receiving, at a first layer of the neural network, an input value and a label associated with the input value. The method further includes calculating, for the first layer of the neural network, a first loss value based at least in part on outputs of the first layer for the input value and for the label. The method further includes updating, based at least in part on the first loss value, the first layer of the neural network based at least in part on the outputs of the first layer for the input value and for the label.

Abstract: This disclosure provides engineered genetic systems for sequestering and/or destroying viruses, compositions and cells comprising the genetic systems, and methods of treating viral infections, reducing viral load, and/or reducing viral spread. The disclosure also provides libraries comprising elements to be incorporated into the engineered genetic systems.

Abstract: A computational system and method for simulating biological tissue includes a computer-modeled 3-dimensional space in which computer-modeled cells are located. The cells may be either healthy or cancerous. As a simulation progresses, the cells may execute a variety of protocols. These include life protocols, such as replication, repair, and apoptosis. They also include signaling protocols, such as a request from a cell to entice defective neighboring cells to die, and an announcement that a cell itself is about to die. The announcement, when received by neighboring cells, tends to entice them also to die. Since defective cells tend to cluster together, the combination of the two signals within a tumor has a strong effect on suppressing the tumor. The simulation tracks the progression of cancerous tumors as well as the effectiveness of various treatments.