Summary: A new study suggests that individual cells may exhibit learning-like behaviors, previously thought exclusive to organisms with nervous systems. Researchers used computational modeling to show how biochemical circuits within cells adapt to repeated stimuli, mimicking habituation—a simple form of learning. Two molecular circuits, negative feedback loops and incoherent feedforward loops, were identified as crucial mechanisms.
This discovery bridges neuroscience and cognitive science debates on habituation while offering insights into phenomena like cancer cell resistance to chemotherapy. The findings highlight the potential of single cells as models for studying learning and memory at its most basic level. The research provides a foundation for future experimental work to confirm these computational predictions.
Key Facts:
- Cells exhibit “learning” behaviors through molecular circuits mimicking habituation.
- Timescale separation in molecular reactions allows cells to adapt and “remember” stimuli.
- These findings could explain how cells adapt in cases like antibiotic or chemotherapy resistance.
Source: Center for Genomic Regulation
Individual cells appear capable of learning, a behaviour once deemed exclusive to animals with brains and complex nervous systems, according to the findings of a new study led by researchers at the Centre for Genomic Regulation (CRG) in Barcelona and Harvard Medical School in Boston.
The findings, published today in the journal Current Biology, could represent an important shift in how we view the fundamental units of life.
“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,” says Jeremy Gunawardena, Associate Professor of Systems Biology at Harvard Medical School, and co-author of the study.
The study looked at habituation, the process by which an organism gradually stops responding to a repeated stimulus. Its why humans stop noticing the ticking of a clock or become less distracted by flashing lights. This lowest form of learning has been studied extensively in animals with complex nervous systems.
Whether learning-like behaviours like habituation exist at cellular scale is a question that’s remained fraught with controversy.
Early 20th-century experiments with the single-celled ciliate Stentor roeselii first shed light on behaviour that resembled learning, but the studies were overlooked and dismissed at the time. In the 1970s and 1980s, signs of habituation were found in other ciliates, and modern experiments have continued to add further weight to the theory.
“These creatures are so different from animals with brains. 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 are able to do this, so we thought it is a question that needed to be explored,” says Rosa Martinez, co-author of the study and researcher at the Centre for Genomic Regulation (CRG) in Barcelona.
Cells rely on biochemical reactions as their means of processing information. For example, the addition or removal of a phosphate tag from the surface of a protein causes it to switch on or off.
To track how cells process information, instead of working with cells in lab dishes, the researchers used computer simulations based on mathematical equations to monitor these reactions and decode the ‘language’ of the cell.
This allowed them to see how the molecular interactions inside cells changed when exposed to the same stimulus over and over again.
Specifically, the study looked at two common molecular circuits – negative feedback loops and incoherent feedforward loops. In negative feedback, the output of a process inhibits its own production, like a thermostat shutting off a heater when a room reaches a certain temperature.
In incoherent feedforward loops, a signal simultaneously activates both a process and its inhibitor, like a motion-activated light with a timer. After detecting movement, the light automatically switches off after a certain period of time.
The simulations suggest that cells use a combination of at least two of these molecular circuits to finetune their response to a stimulus and reproduce all the hallmark features of habituation seen in more complex forms of life.
One of the key findings is a requirement for “timescale separation” in the behaviour of the molecular circuits, where some reactions happen 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.
The finding may also illuminate a longstanding debate between neuroscientists and cognitive researchers. For years, these two groups have had different takes on how habituation strength relates to the frequency or intensity of stimulation. Neuroscientists focus on observable behaviour, noting that organisms show stronger habituation with more frequent or less intense stimuli.
Cognitive scientists, however, insist on testing for the existence of internal changes and memory formation after habituation has taken place. When following their methodology, habituation seems stronger for less frequent or more intense stimuli.
The study shows that the behaviour of the models aligns with both views. During habituation, the response decreases more with more frequent or less intense stimuli, but after habituation, the response to a common stimulus is also stronger in these cases.
“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.”
The research deepens our understanding of how learning and memory operate at the most basic level of life. If single cells can “remember,” it could also help explain how cancer cells develop resistance to chemotherapy or how bacteria become resistant to antibiotics — situations where cells seem to “learn” from their environment.
However, the predictions need to be confirmed with real-world biological data. The study used mathematical modelling to explore the concept of learning in cells because it let them test many different scenarios rapidly to see which ones are worth investigating further in real experiments.
The work could lay the foundation for experimental scientists to now design lab experiments and test these predictions.
“The moonshot in computational biology is to make life as programmable as a computer, but lab experiments can be costly and time-consuming,” says Dr. Martinez, who is based at the Barcelona Collaboratorium, a joint initiative between the CRG and EMBL Barcelona specifically designed to advance research based on mathematical modelling to address big questions in biology.
“Our approach can help us prioritise which experiments are most likely to yield valuable results, saving time and resources and leading to new breakthroughs,” she adds. “We think it can be useful to address many other fundamental questions.”
About this genetics and neuroscience research news
Author: Omar Jamshed
Source: Center for Genomic Regulation
Contact: Omar Jamshed – Center for Genomic Regulation
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Biochemically plausible models of habituation for single-cell learning” by Jeremy Gunawardena et al. Current Biology
Abstract
Biochemically plausible models of habituation for single-cell learning
The ability to learn is typically attributed to animals with brains. However, the apparently simplest form of learning, habituation, in which a steadily decreasing response is exhibited to a repeated stimulus, is found not only in animals but also in single-cell organisms and individual mammalian cells.
Habituation has been codified from studies in both invertebrate and vertebrate animals as having ten characteristic hallmarks, seven of which involve a single stimulus.
Here, we show by mathematical modeling that simple molecular networks, based on plausible biochemistry with common motifs of negative feedback and incoherent feedforward, can robustly exhibit all single-stimulus hallmarks.
The models reveal how the hallmarks arise from underlying properties of timescale separation and reversal behavior of memory variables, and they reconcile opposing views of frequency and intensity sensitivity expressed within the neuroscience and cognitive science traditions.
Our results suggest that individual cells may exhibit habituation behavior as rich as that which has been codified in multi-cellular animals with central nervous systems and that the relative simplicity of the biomolecular level may enhance our understanding of the mechanisms of learning.
