EAGER: Closed-loop Bioelectronic Control of Computing through Bacterial Gene Regulatory Artificial Neural Networks

University Of Nebraska-Lincoln, Lincoln NE

Investigators

Abstract

Society is entering into a new era of technology that is dominated by artificial intelligence (AI). While conventional AI has been inspired from biology, recent studies suggest that biological cells themselves can be directly connected to computers as AI machines to harness the intelligence inherent in living cells. This project aims to discover natural AI structures in bacterial gene regulatory networks that can be leveraged for computing applications. This project seeks to develop a bio-hybrid computing system, where the gene regulatory networks of bacteria are used to perform AI computing. The team of researchers will establish an electrical - chemical - electrical communication mechanism, where information signals from a computer will stimulate bacterial gene regulatory networks to perform chemical-based computing. The output of this network will produce electrical signals that can be interpreted by a computer. By offloading computing to the bacteria, the impact of this research may transform the design of energy-efficient computing architectures with novel implications for healthcare and environmental sensing. This research will enhance both the PI and Co-PI's curriculums in Molecular and Nanoscale Communications and Environmental Biotechnology. In addition, this work will support multidisciplinary trainee-training to prepare the biotechnology workforce. Using the electroactive bacterium, Shewanella oneidensis, the project seeks to utilize gene regulatory artificial neural networks (GR-ANN) by extracting subnetworks to perform computing applications. The GR-ANN subnetworks will be operated by electrogenetic stimulation of input genes, while the output genes are engineered to produce redox active molecules that can be sensed electrically. As proof-of-concept, the project will demonstrate two forms of mathematical computation that includes multiplication function and prime number classification. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.