Expanding biological control to bioelectronics with machine learning

• Published in: APL materials Vol. 8; no. 12; pp. 120904 - 120904-6
• Main Authors: Selberg, J., Jafari, M., Bradley, C., Gomez, M., Rolandi, M.
• Format: Journal Article
• Language: English
• Published: AIP Publishing LLC 01.12.2020
• ISSN: 2166-532X; 2166-532X
• DOI: 10.1063/5.0027226

Bioelectronics integrates electronic devices and biological systems with the ability to monitor and control biological processes. From homeostasis to sensorimotor reflexes, closed-loop control with feedback is a staple of most biological systems and fundamental to life itself. Apart from a few examples in bioelectronic medicine, the closed-loop control of biological processes using bioelectronics is not as widespread as in nature. We note that adoption of closed-loop control using bioelectronics has been slow because traditional control methods are difficult to apply to the complex dynamics of biological systems and their sensitivity to environmental changes. Here, we postulate that machine learning can greatly enhance the reach of bioelectronic closed-loop control and we present the advantages of machine learning compared to traditional control approaches. Potential applications of machine learning-based closed-loop control with bioelectronics include further impact in bioelectronic medicine and fine tuning of reactions and products in synthetic biology.