An interdisciplinary team of researchers from the University of California, Santa Cruz has achieved an unprecedented control of cell membrane voltage.
The team which also includes researchers from Tufts University has developed a bioelectronic system driven by a machine learning algorithm that can shift the membrane voltage in living cells and maintain it at a set point for 10 hours.
The research appeared yesterday in the Advanced Intelligent Systems journal.
What is membrane voltage?
Membrane voltage is the difference in electric potential between the interior and the exterior of a biological cell. It is also known as membrane potential. This potential results from differences in the concentrations of charged ions inside and outside the cell. The typical values of this potential range from –40 mV to –80 mV.
As we all know that biology at the cell level is pretty sensitive, thus, controlling this potential using bioelectric systems is very difficult. Our cells respond to changes in their environment in a complex way. Cells also have a natural self-regulating feedback process known as homeostasis and they regulate ion movement to maintain a constant membrane voltage.
Thus, overcoming these natural processes and safely controlling the membrane voltage is a challenge.
“Biological feedback systems are fundamental to life, and their malfunctioning is often involved in diseases. This work demonstrates that we can tweak this feedback using a combination of bioelectronic devices actuated by machine learning, and potentially restore its functioning,” said Marco Rolandi, professor and chair of electrical and computer engineering at the UC Santa Cruz Baskin School of Engineering.
The system which was showcased as a proof-of-concept involves an array of bioelectronic proton pumps that add or remove hydrogen ions from solution in proximity to cultured human stem cells. The cells were genetically modified to express a fluorescent protein on the cell membrane that responds to changes in membrane voltage. The system is controlled by a machine learning algorithm that tracks how the membrane voltage responds to stimuli from the proton pumps.
“It is a closed-loop system, in that it records the behaviour of the cells, determines what intervention to deliver using the proton pumps, sees how the cells react, then determines the next intervention needed to achieve and maintain the membrane voltage status we desire,” Rolandi explained.
Marcella Gomez, the person behind the machine learning algorithm explained that their model learns in real-time as the neural network responds to input regarding the current state of the membrane voltage.
This study is a very good proof of the concept of controlling cell functions using biolelectric devices and machine learning algorithms.
John Selberg, Mohammad Jafari, Juanita Mathews, Manping Jia, Pattawong, Pansodtee, Harika Dechiraju, Chunxiao Wu, Sergio Cordero, Alexander, Flora, Nebyu Yonas, Sophia Jannetty, Miranda Diberardinis, Mircea Teodorescu, Michael Levin, Marcella Gomez, Marco Rolandi. Machine Learning‐Driven Bioelectronics for Closed‐Loop Control of Cells, Advanced Intelligent Systems (2020). DOI: 10.1002/aisy.202000140
Press Release: University of California – Santa Cruz