According to groundbreaking new findings, single cells may be capable of learning without the need for complex brains and nervous systems.
Researchers from the Centre for Genomic Regulation (CRG) in Barcelona and Harvard Medical School in Boston recently published their work in Current Biology. Their work presents insights that may affect the future of medicine, providing a deeper understanding of how specific ailments can avoid treatment.
CElls Learning from Their Environment
“Rather than following pre-programmed genetic instructions, cells are elevated to entities equipped with a very basic form of decision-making based on learning from their environments,” explained co-author Jeremy Gunawardena, Associate Professor of Systems Biology at Harvard Medical School.
The biologists’ observations involved habituation, one of the simplest forms of learning, where an organism acclimates to a repeated stimulus and begins to ignore it. Examples include ticking clocks or flashing lights, stimuli that eventually fade into the background for humans as our perceptions start ignoring them after some time.
Since the early 20th century, biologists have debated studies indicating learning-like behaviors in single-celled ciliates. The search picked up steam in the 1970s and 1980s, and current research provides additional mounting evidence for cell learning capabilities.
Examining Cell Learning
“These creatures are so different from animals with brains,” says co-author Rosa Martinez of the Centre for Genomic Regulation (CRG) in Barcelona. “To learn would mean they use internal molecular networks that somehow perform functions similar to those carried out by networks of neurons in brains.”
“Nobody knows how they can do this, so we thought it is a question that needed to be explored,” Martinez said.
Cells process information through biochemical reactions, such as adding or removing a phosphate tag to a protein to switch it on and off like a binary code. The team modeled those chemical interactions in a computer simulation. The biologists chose this method because it allowed them to test many scenarios more rapidly than setting up many observations. Analyzing the math permitted the researchers to decode the cell’s chemical language as responses to repeated stimuli changed over time.
The biologists focused on negative feedback loops and incoherent feedforward loops to help better understand how the cells processed information and reacted. Negative feedback loops describe information that signals a process should end, like a thermostat registering the desired temperature and turning off the heat. In an incoherent feedforward loop, a signal turns a process both on and off, such as when a motion-activated light turns on after registering movement but turns off again after a set amount of time elapses.
Evidence of Cellular Memory
The data produced in the simulations demonstrated that cells were combining both types of circuits to develop fine-tuned responses, including many features of habituation typically recorded in more complex lifeforms. Intriguingly, some reactions occur much faster than others.
“We think this could be a type of ‘memory’ at the cellular level, enabling cells to both react immediately and influence a future response,” explains Dr. Martinez.
Solving an Old Divide
The research may also help bridge the gap between cognitive scientists’ and neuroscientists’ positions on habituation. Relying on observable outside behavior, neuroscientists point out that higher frequency and less intense stimuli correlate to greater habituation. Cognitive scientists’ interest in internal changes and memory formation suggests the opposite: that less frequent and more intense stimuli generate stronger habituation.
The new research illuminates that something more nuanced is occurring. While the organism is habituating, more frequent and less intense stimuli lead to decreasing response. Yet after habituating, it more strongly reacts to the same high-frequency, lower-intensity stimuli.
“Neuroscientists and cognitive scientists have been studying processes which are basically two sides of the same coin,” says Gunawardena. “We believe that single cells could emerge as a powerful tool to study the fundamentals of learning.”
Future Medical Applications
The work’s potential value is tremendous. From a medical science perspective, the ability of cancer cells to develop chemotherapy resistance or bacteria’s ability to develop antibiotic resistance is a major concern. However, understanding if and how cells learn could significantly enhance the effectiveness of such treatments. While further research is needed to confirm the modeled predictions using real-world observations, the initial results are promising.
“The moonshot in computational biology is to make life as programmable as a computer, but lab experiments can be costly and time-consuming,” Martinez explained.
“Our approach can help us prioritize which experiments are most likely to yield valuable results, saving time and resources and leading to new breakthroughs,” she added.
“We think it can be useful to address many other fundamental questions,” Martinez said.
The paper “Biochemically Plausible Models of Habituation for Single-Cell Learning” appeared on November 21, 2024, in Current Biology.
Ryan Whalen covers science and technology for The Debrief. He holds a BA in History and a Master of Library and Information Science with a certificate in Data Science. He can be contacted at ryan@thedebrief.org, and follow him on Twitter @mdntwvlf.