Living optical random neural network with three dimensional tumor spheroids for cancer morphodynamics

• Published in: Communications physics Vol. 3; no. 1
• Main Authors: Pierangeli, D., Palmieri, V., Marcucci, G., Moriconi, C., Perini, G., De Spirito, M., Papi, M., Conti, C.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15-09-2020; Nature Publishing Group
• Subjects: 639/624/400/385; 639/766/747; Artificial intelligence; Artificial neural networks; Biological materials; Cancer; Chemotherapy; Hyperthermia; Image classification; Ising model; Light speed; Machine learning; Neural networks; Object recognition; Optical communication; Physics; Physics and Astronomy; Speech recognition; Spheroids; Three dimensional models; Toxicity; Tumors
• ISSN: 2399-3650; 2399-3650
• DOI: 10.1038/s42005-020-00428-9

Optical neural networks process information at the speed of light and are energetically efficient. Photonic artificial intelligence allows speech recognition, image classification, and Ising machines. Modern machine learning paradigms, as extreme learning machines, reveal that disordered and biological materials may realize optical neural networks with thousands of nodes trained only at the input and at the readout. May we use living matter for machine learning? Here, we employ living three-dimensional tumor brain models to demonstrate a random optical learning machine (ROM) for the investigation of glioblastoma. The tumor spheroid act as a computational reservoir. The ROM detects cancer morphodynamics by laser-induced hyperthermia, quantifies chemotherapy, and cell metabolism. The ROM is a sensitive noninvasive smart probe for cytotoxicity assay and enables real-time investigation of tumor dynamics. We hence design and demonstrate a novel bio-hardware for optical computing and the study of light/complex matter interaction.
Can living systems function as artificial neural networks for biophysical applications? Here, the authors show that living tumor spheroids can be employed as random optical learning machines and used to investigate cancer morphodynamics and quantify the effect of chemotherapy.