Machine Learning‐Driven Bioelectronics for Closed‐Loop Control of Cells

Abstract

From the simplest unicellular organisms to complex animals, feedback control based on sensing and actuation is a staple of self‐regulation in biological processes and is a key to life itself. Malfunctioning of this control loop can often lead to disease or death. Bioelectronic devices that interface electronics with biological systems can be used for sensing and actuation of biological processes and have potential for novel therapeutic applications. Due to the complexity of biological systems and the challenge of affecting their innate self‐regulation, closing the loop between sensing and actuation with bioelectronics is difficult to achieve. Herein, bioelectronic proton‐conducting devices are integrated with fluorescence sensing using machine learning to provide closed‐loop control of bioelectronic actuation in living cells. Proton‐conducting bioelectronic devices control pH in a microfluidic system that houses pluripotent mammalian stem cells. This pH control affects the membrane voltage (Vmem) of the cells that is measured using genetically encoded fluorescent Vmem reporters. In this fashion, proof‐of‐concept real‐time control of Vmem toward a desired set‐point is demonstrated. Given the importance of Vmem in cell function, differentiation, and proliferation, this proof‐of‐concept opens up many possibilities in bioelectronic closed‐loop control of cell systems.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 24, 2020
Source ID
10.1002/aisy.202000140

Entities

People

  • Alexander Flora
  • Chunxiao Wu
  • Harika Dechiraju
  • John Selberg
  • Juanita Mathews
  • Manping Jia
  • Marcella M Gomez
  • Marco Rolandi
  • Michael Levin
  • Miranda Diberardinis
  • Mircea Teodorescu
  • Mohammad Jafari
  • Nebyu Yonas
  • Pattawong Pansodtee
  • Sergio Cordero
  • Sophia Jannetty

Organizations

  • Army Research Office
  • Defense Advanced Research Projects Agency
  • Harvard University
  • Tufts University
  • University of California, Santa Cruz

Tags

Readers

  • Immunology and Pathology
  • Nanoscale Plasmonic Nanotechnology
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • Biotechnology
  • Microelectronics
  • Microelectronics - Microelectromechanical Systems