Open AccessMachine Learning‐Driven Bioelectronics for Closed‐Loop Control of Cells
Author(s) -
Selberg John,
Jafari Mohammad,
Mathews Juanita,
Jia Manping,
Pansodtee Pattawong,
Dechiraju Harika,
Wu Chunxiao,
Cordero Sergio,
Flora Alexander,
Yonas Nebyu,
Jannetty Sophia,
Diberardinis Miranda,
Teodorescu Mircea,
Levin Michael,
Gomez Marcella,
Rolandi Marco
Publication year - 2020
Publication title -
advanced intelligent systems
Language(s) - English
Resource type - Journals
ISSN - 2640-4567
DOI - 10.1002/aisy.202070122
Subject(s) - bioelectronics , function (biology) , membrane , nanotechnology , voltage , loop (graph theory) , electrical engineering , engineering , computer science , chemistry , materials science , biology , biosensor , microbiology and biotechnology , mathematics , combinatorics , biochemistry
Bioelectronics In article number 2000140 , Michael Levin, Marcella Gomez, Marco Rolandi, and co‐workers present bioelectronics coupled with machine learning for intelligent control of cellular function. The authors set and maintain the membrane voltage of human pluripotent stem cells with H + ‐conducting bioelectronic devices. The H + ‐devices are actuated in a closed feedback loop and respond to required adjustments to the cell membrane voltage as measured with fluorescent reporters.