Sea Monkeys Show Scientists How To Rewrite a Rule of Turbulence

Mike's Notes

Fascinating. 😎

What a cool experiment to do. From Quanta Magazine.

Resources

• https://www.quantamagazine.org/sea-monkeys-show-scientists-how-to-rewrite-a-rule-of-turbulence-20261002/
• https://www.engineering.pitt.edu/people/faculty/lei-fang/
• https://www.researchgate.net/profile/Francesca-De-Serio
• https://academic.oup.com/pnasnexus/article/3/2/pgae056/7603763
• https://doi.org/10.1093/pnasnexus/pgae056
• https://www.science.org/doi/10.1126/sciadv.adv0956
• https://journals.aps.org/prfluids/abstract/10.1103/mkwg-hmgn

References

• Biologically generated turbulent energy flux in shear flow depends on tensor geometry by Xinyu Si, Lei Fang. PNAS Nexus, Volume 3, Issue 2, February 2024.

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Last Updated

30/10/2026

Sea Monkeys Show Scientists How To Rewrite a Rule of Turbulence

By: Stephen Ornes

Quanta Magazine: 02/10/2026

Stephen Ornes: Contributing writer

...

Scientists assumed that energy flows in only one direction in a turbulent system. What they didn’t know, until they looked closely at brine shrimp, was that a simple factor can reverse the flow.

...

In Lei Fang’s lab at the University of Pittsburgh, a brine shrimp swims through a two-dimensional turbulent system.

Xinyu Si/University of Pittsburgh

Introduction

In the 1960s and ’70s, colorful comic book advertisements from the Transcience Corporation in New York City promised to mail anyone who sent cash, check, or money order a “bowlfull of happiness” — in reality, a small paper envelope of freeze-dried eggs. Dropped into salt water, the eggs would produce brine shrimp, also known as sea monkeys.

Brine shrimp are about a centimeter long and swim upside down, beating their legs madly and trailing their elongated abdomens behind them like tails. As scientists recently learned, these tiny swimmers can do more than just move themselves.

Researchers have long thought that in a turbulent system like a current of water, energy flows in only one direction, from larger scales to smaller ones, or vice versa, depending on the system’s dimensions. But in observing the humble brine shrimp, scientists at the University of Pittsburgh realized that — with just a tiny adjustment — the flow of energy could be reversed.

The scientists discovered that they could direct the cascade of energy in a two-dimensional system by disrupting the system’s flow with a small obstacle, as long as it was angled just right. “The geometry matters,” said Lei Fang, the Pittsburgh engineer who led the new study.

The discovery capitalized on a fundamental, if often unrecognized, mathematical description of how forces interact to push energy through a system, said Gregory Falkovich, a physicist at the Weizmann Institute of Science and a pioneer in the study of turbulent two-dimensional systems. “This is a beautiful and skillful experimental work.”

These findings could extend to larger, more chaotic systems, including those in three dimensions. The work has implications for our understanding of fluid dynamics and may have applications in areas such as pollution control and drug design.

A translucent white invertebrate with red eyes and a long tail.

Brine shrimp are tiny invertebrates that inhabit waters with high salt concentration. They grow to about 10 millimeters long, the width of a pencil eraser.

Nature Picture Library/Alamy

A Cascade of Chaos

Turbulence is hard to miss. It stirs the raucous foam at the base of a waterfall and shapes the crest of a breaking wave. It’s behind the bumpiness of a flight, the swirls of milk in a cup of coffee, and the roil of plasma on the surface of the sun. “We observe turbulence generally everywhere in our lives, when we go to the beach or wash our hands,” said Francesca De Serio, a civil engineer and expert on hydrodynamics at the Polytechnic University of Bari in Italy.

"We observe turbulence generally everywhere in our lives, when we go to the beach or wash our hands." - Francesca De Serio, Polytechnic University of Bari

A turbulent system is complicated, characterized by complex forces and inner turmoil. But it can start simply enough. For example, turbulence arises when a flowing fluid encounters an obstacle that changes the velocity of part of the flow. In a river, water slows down by the bank due to friction, and the velocity mismatches can produce vortices or eddies. When a river divides to move around a rock, speeds change and voids form; water falls over itself and may start to rotate. Air, too, is a fluid, its turbulence produced by competing currents and changing temperatures.

In such a system, energy moves between size scales. English mathematician and physicist Lewis Fry Richardson, the founder of modern weather forecasting, discovered this when he studied the fluid systems formed by gases in the atmosphere. He found that the kinetic energy of a large eddy feeds into smaller and smaller eddies, until it reaches the scale where viscosity, which resists motion, takes over. The energy is ultimately dissipated as heat. In 1922, Richardson captured the concept in verse:

Big whorls have little whorls

Which feed on their velocity,

And little whorls have lesser whorls

And so on to viscosity.

Starting in the early 1940s, the Soviet mathematician Andrey Kolmogorov provided a clear and rigorous mathematical foundation for the study of turbulence. But his work, like Richardson’s, mainly focused on three-dimensional systems in which energy flows from large features to small ones.

In the late 1960s, the physicists Robert Kraichnan and George Batchelor extended the investigation to two-dimensional systems. Two-dimensional turbulent systems include phenomena like Jupiter’s Great Red Spot, which swirls so violently that it overwhelms any motion farther down in the atmosphere. This type of two-dimensional turbulent system transfers energy from one scale to another, like a three-dimensional one, but with a twist: The energy of a two-dimensional turbulent system cascades in the opposite direction, moving from small eddies up to large ones. In Jupiter’s Great Red Spot, smaller eddies and vortices near the perimeter feed the giant maelstrom in the center.

In the decades after those pioneering studies reported an inverse flux in two-dimensional systems, physicists did not deeply interrogate whether those flows were fixed. The underlying energy mechanisms of two-dimensional turbulent systems certainly weren’t on the mind of Xinyu Si around 2021, when he started investigating brine shrimp as a student in Fang’s lab.

Swimming in the Energy Flow

Fang’s lab focuses on “active matter,” a physics term describing things that move by themselves and inject energy into their environments. It’s a label broad enough to include networks of living things, like bacteria, and nonliving things, like tiny robots.

In this project, Si and Fang were interested in investigating how biological swimmers mix materials in fluids where turbulence shows up. The researchers had hypothesized that, in the mixing of fluids in large natural systems, turbulence generated by tiny organisms plays an underappreciated role. Billions of minuscule creatures, after all, agitate the waterways of the world.

In a laboratory lit with red light, a man holds a tube over a tank filled with cloudy liquid.

Xinyu Si, now a postdoc at the University of Rochester, prepares a research tank.

J. Adam Fenster/University of Rochester

To closely study this in a simple model, Si poured a thin film of salt water over a glass surface in a tank and positioned magnets underneath. When he applied an electric current to the conductive fluid, it interacted with the magnets and created motion in the water, which flowed in one direction along one side of a channel and in the opposite direction along the other side. This created a gradient of velocities across the center, to which Si added brine shrimp.

When Si and Fang calculated how the shrimp’s flailing bodies disrupted the moving water, they saw “something very, very strange,” Fang said: The shrimp weren’t just contributing to the chaos of the system; they also seemed to be shifting the direction in which energy flowed.

A turbulent system can be described in terms of how much it stretches and compresses the fluids within, due to the interactions of fluids flowing at different velocities. These dynamics show up, for example, when you pour confetti into a river and watch it organize into sweeping lines as it races on competing currents downstream.

"Every experiment suggested the same conclusion: The orientation of a brine shrimp’s body seemed to change the turbulent system’s energy flux."

Si and Fang ran the brine shrimp experiment repeatedly. They found that when a solo shrimp swam at an angle of less than 45 degrees to the direction of maximum stretching, the energy flowed from smaller scales to larger ones, feeding the flow itself, as they’d predicted for a 2D system. But if the shrimp swam at an angle of more than 45 degrees to the direction of maximum stretching, the energy went in reverse, pulling from the larger scale of the flow to energize smaller perturbations.

It appeared to be a clean about-face. Every experiment suggested the same conclusion: The orientation of a brine shrimp’s body seemed to change the turbulent system’s energy flux.

The scientists found a way to interpret what they were seeing through a field of mathematics called tensor geometry, which has long been a workhorse for physicists studying turbulence. A tensor is a mathematical tool that can help you measure something that changes in multiple ways at once. For example, when you press down on a sponge, its sides bulge outward. A tensor can describe how stress — the weight of your hand — affects strain — the deformation of the sponge. Tensors give fluid dynamics researchers a powerful way to model physical phenomena that can transform in terms of direction or strength.

Fang and Si found that their brine-shrimp situation made sense if they looked at it as the interaction of two tensors in two dimensions. One tensor represented how shear — the large-scale movement of the water — affected energy flux. The other represented how the energy flux was shaped by small-scale movements from the swimmers.

The orientation of the swimmer determined the alignment of the two tensors, and the alignment of the tensors determined how the energy flowed. As Fang and Si reported in 2024, the angle between the brine shrimp’s body and the forces of the fluid could alter the direction of the energy flux.

Turning the Tide

Portrait of a man in a sweater and tie.

Fang, an engineer at the University of Pittsburgh, is interested in the influence of biological swimmers in natural systems.

University of Pittsburgh

Si had been releasing brine shrimp into the tank, pointing a camera at them, and patiently waiting until one swam through the test area at just the right angle. In the next iteration of the experiment, Si and Fang replaced the brine shrimp with an array of centimeter-long rods, which proved easier to measure. They found that, by altering the orientation of the rods relative to the flow, they were able to control whether energy transferred to higher or lower scales.

The findings held even when Si and Fang invited the scrutiny of two other scientists: Filippo De Lillo and Guido Boffetta of the University of Turin in Italy, whom Fang met at a conference. In the outside experts’ numerical simulations, the same effect clearly showed up. “That made me much more comfortable,” Fang said, “because it’s a very weird thing to manipulate energy flux.”

With renewed confidence, the researchers reported their findings(opens a new tab) in 2025. “It was interesting because they were saying that with small, specific interventions in the flow we can … show a transfer of energy between scales,” De Serio said. They demonstrated that with minimal manipulation, the energy flux in a 2D system can be steered to act more like (but not exactly like) the flux in a 3D one, which suggests that physicists might be able to apply insights from one dimensional domain to the other.

There are confounding issues to overcome. In their experiments, Si and Fang used a shallow pool of fluid whose friction made turbulence hard to initiate. And there are big differences between a weakly turbulent two-dimensional system like the one in their lab and the strongly turbulent three-dimensional ones more commonly found in nature.

Mathematically, though, there’s no reason the scientists’ observations shouldn’t extend to such scenarios. Si and Fang are investigating the possibility, testing ideas on tabletop tornadoes.

They’re also exploring how to influence the “transport barrier,” the threshold between two fluids as they mix. According to Fang’s theory, disrupting this boundary — which could be useful in controlling pollution or mixing fluids for drug development — would use less than 1% of the energy required to keep the boundary intact.

Other scientists have also begun experimenting with manipulating energy flux. De Serio, at the Polytechnic University of Bari, has been firing strong jets into large tanks of turbulent, rotating fluids with high viscosity. “I was attracted by this idea that these [manipulations] could be applied in a real context,” she said. In March 2026, she reported results from experiments conducted on a large rotating vortex manipulated with small, horizontal jets firing into the middle. She observed patches of inverse energy flux that started out as intermittent bursts but grew steady with increasing rotation. Those findings, she says, may point to practical strategies for controlling energy pathways in real-world situations.

Si, now a postdoc at the University of Rochester, said that the current work introduces a new hypothesis that’s testable in any setting where turbulence plays a role, from a tiny “bowlfull of happiness” to the enormous scale of Earth’s atmosphere. Turbulent systems may be notoriously difficult to understand, but what dictates the flow of energy may be an issue of simple geometry.

Visualizing the systems behind our designs

Mike's Notes

A toolbox of diagrams for visual thinking about systems.

Resources

• https://uxdesign.cc/visualizing-the-systems-behind-our-designs-7a7c95b4cfb2
• https://www.nasa.gov/johnson/HWHAP/the-overview-effect

References

• Reference

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Last Updated

26/10/2026

Visualizing the systems behind our designs

By: Justin Farrugia

UX Collective (Medium): 25/02/2020

Justin Farrugia: Editor of Casumo Design and Soundbite.

...

Tangible systems thinking that goes beyond buttons.

A button on the left which is what we see, and a complex casual loop diagram on the right which is what’s actually happening

Systems thinking is often heralded in different circles as a competency that’s worth investing in. Having a wider lens and being able to model the systems that influence inputs, outputs and the patterns of behavior for people using the products we build is how we can deliver real value.

Most product designers tend to default to design systems as a proxy for systems thinking. They’re not entirely wrong. The ecology around building design systems does require maintainers to think systematically. They need to deeply understand the core needs of the product team, to deliver reusable component libraries coupled with accompanying guidelines, processes, and documentation.

However, this doesn’t mean that building a design system is the only natural outcome of thinking in systems. Far from it.

Beyond buttons

As of writing, few people in history have had the surreal experience of being able to witness the Earth from space in person. Astronauts often recall the overwhelming experience they felt when looking at the fragile blue ball they called home. Their front row seats enabled them to see the world through a different lens, one where all life was interconnected and every system they’ve come to know was working in concert.

Astronomy author Frank White called this experience, ‘The Overview effect’.

Whilst not nearly as life-changing of an experience, zooming out from our narrowly focused product domains and realizing every design decision’s ripple effect in the ecosystem we operate in, can have a similar effect. Designing software often requires us to situate ourselves at both a macro and micro level. Yet, sometimes there’s a tendency to over-index on the latter at the expense of a broader set of goals.

As a result, we risk becoming distracted with the details when we can’t afford to do so. The tools, products and service experiences we’re shaping today are responsible for far more than just the daily metric movements relative to the bottom line. They inhabit already complex systems such as political landscapes or socioeconomic hierarchies and sometimes unconsciously introduce new dynamics that have outsized impacts.

"We are surrounded by systems that we’ve directly or indirectly shaped or been a product of."

That being said we’ve done very little to represent any of them. By doing so we can peel back the layers and uncover patterns, boundaries, behaviors and even points of leverage where we can infer our influence. The good news is that we have an underdeveloped muscle which we ought to flex more often that can help us out.

Tools of the trade

We often picture systems in a linear fashion where A leads to B. Needless to say this is a reductive way of describing relationships and causality, as it overlooks the fact that each node may disrupt or restore the balance in uniquely different ways. In other words, systems are messy.

Linear Systems Thinking versus Loop Based Systems Thinking

Instead of A leading to B, it’s more likely you’ll find something like, A can cause B and C given a set of conditions, both of which reinforce D which restarts the cycle. Now trying to articulate all that through text isn’t exactly the best way to explain what’s actually going on. That’s where system diagramming can come in handy. Below are a few examples.

Behavior Over Time Graph

You’ve probably already seen this type of diagram being plotted somewhere on an office whiteboard. This graph helps us see patterns and interrelationships that emerge over a specific period of time. We can use it to observe how and why something is happening.

Let’s use something we’re all too familiar with as an example — product debt. Two things that we can use as variables in our graph are the cost of building features which keeps rising exponentially as the product matures, and customer value delivered which is inversely proportional due to fragmentation and debt that the team is spending time wrestling with.

A behaviour over time graph that indicates when a product team should address its mounting debt.

Despite ancillary efforts to revive that delivery curve from declining further, it most likely wouldn’t be enough to curb the overhead. The intersection point between the two curves signals the necessity for a cadence of refactoring or even a commitment towards building a design system.

Quadrant Matrix

This is another common diagram, that also has likely donned a few whiteboards. It is mainly used to represent the strength of relationships between several different items. You’ve likely seen it being used to chart the impact versus effort of doing something in a bid to inform the decision-making process.

A quadrant of usability issues with repetition versus impact

Let’s say you’ve finished up a moderated usability testing session and you’ve come away with a few insights. To start applying what you’ve learned you’ll need to rank your findings against a few different dimensions namely repetition and impact. Using a quadrant matrix will help you synthesize the raw data you’ve collected and visually make sense of what issues were the most severe and iron them out through iteration.

Iceberg Model

The iceberg model is a tool that can be used to show the four levels of abstraction within a specific setting that can allow us to uncover the root cause of why something is actually happening the way it is. It helps us go beyond the observable events that anyone can notice, and help us realize the patterns that might have contributed to those events, the structures that have influenced those patterns and the underlying mental models that keep those structures in place.

Often times when we try to address a problem, we do it on an event level which in turn leads to suboptimization. It’s not enough to swap things around, change information flow or incentives. You’ll need to expand the perception and shift mental models to have leverage and upend the system.

Let’s take a real-world example of a product team that’s trying to meet a deadline. An event would be that the team doesn’t ship in time. The natural tendency is to be reactive and start to find people to blame. Instead of playing the blame game start looking at the trends that might have led to this.

The Iceberg Model representing the Events, Patterns, Structures and Mental Models behind a product team missing a deadline.

A pattern might be looking at recurring evidence like the team’s collective track record and finding that three of the last five things they’ve worked on have had overrun dates. The structures that might be causing these patterns could be bandwidth issues with engineers and designers needing to context switch due to commitments outside the team.

What’s keeping this system in place is the mental model each team member holds, which might normalize the balancing act of being first responders instead of establishing a cadence coupled with some boundaries.

Reinforcing Feedback Loops

However intentional it might have been, you’ve probably designed a reinforcing feedback loop. As the name suggests, these kinds of loops have self-reinforcing behavior where the actions of one variable reinforce another. I’d be remiss to not mention the Hooked model as an example of a reinforcing loop that optimizes user stickiness and engagement.

The Hooked Model by Nir Eyal.

Unsurprisingly, reinforcing feedback loops are used and abused. Most consumer products with some kind of social component and a healthy network effect have reinforcing feedback loops baked into the fabric of the core experience. Products like TikTok have predictive models, computer vision and matrix factorization algorithms each built-in to perfect the content recommendation loop and have our attention.

TikTok’s reinforcing feedback loop.

Even though it has a cold start problem, after only a few seconds of watching and a series of swipes TikTok has collected enough data points and spun that loop so many times, that the content suddenly becomes unmissable. The diagram above illustrates this example.

Balancing Feedback Loops

Instead of creating a snowball effect, balancing feedback loops counter an input with an opposite output. These loops can correct an oversupply or undersupply, indicate that a plateau has been reached or restore the lost stability back to the system. Like reinforcing feedback loops however, the outcome of balancing feedback loops might not always lead to positive outcomes depending on prior inputs.

Some teams seem to jump the gun when it comes to bridging the gap between their customer success representatives and the customers. Access to an agent is either obscured by a messy navigation system or else it’s available at all times even when unnecessary. Both are examples of a balancing feedback loop.

The ramifications of introducing a chat bubble representing as a balancing feedback loop.

The former being the increase in customer frustration in trying to flag an issue by jumping through a few hoops which leads to an inevitable decrease in experiential and customer satisfaction measures. The latter shows a complete disregard for the time spent in answering repeatable queries which increases given there is no FAQ-like system in place which in turn leads to customer success agents availability decreasing, diminishing the likelihood of being a first responder for high-value accounts.

External Factors, Gaps, and Delays in Causal Loops

In the ever-increasing complexity of the systems we build towards, loops rarely ever have equally defined units of input or output. There are usually things that happen in between or outside of the model that have an effect. What better way to represent concepts like gaps and delays than an unpredictable complex landscape like healthcare, specifically the use of software in aiding care decisions.

Electronic Health Records or EHRs are real-time robust systems used in hospitals that contain the medical history of a patient. Their goal is to ultimately chip away at the mundanity of paperwork and enable a meaningful patient to physician interaction. If you zoom out far enough, the most basic loop you can make out is the one where the effects of introducing an EHR into a previously paper-run practice become visible.

Diagram of a reinforcing feedback loop that shows the effects of introducing an Electronic Health Record or EHR.

As can be seen in the diagram above, the impact of introducing an EHR into the mix has saved staff time which in turn has increased the patient treatment quality. As a result, the better the treatment quality, the more time will be saved in preventing adverse drug events or readmissions.

Diagram representing a delay within a causal loop.

Now that’s not going to happen overnight, the staff on hand needs to be gradually onboarded onto the new system and get familiar. That’s why this feedback loop will likely have delays which we can represent by marking the arc with a double line.

Diagram representing an external factor, in this case an update to the software.

Our feedback loop is still looking a bit too convenient. Let’s introduce another component, such as an update to the EHR software. That in itself might eat up some of that time the staff is saving since there might need to be retraining. That’s called an external factor. Gaps can also be another feature of causal feedback loops. A gap can exist between the actual quality and the actions we’re taking to improve that quality.

Representing Gaps in EHR causal loops

In our example for patient treatment quality to improve, the gap in between, as shown in the diagram, is the secure migration of the patient records from paper to digital records. This, in turn, enables more confidence to adopt and inevitably invest in the software beyond just procurement, but also designing training programs for practitioners and defining performance targets such as decreasing error rates or patient treatment time.

Cause and Effect Diagram, Onion Model Diagram and a Hierarchy

These are just a select few of the tools available in your systems utility belt. Needless to say, there’s an abundance of other ways you can visualize systems. If you’re trying to bring clarity to a post mortem, you can use a cause and effect diagram to isolate what went wrong during a project. A stock and flow diagram can help you simulate the impact of your design decisions, whereas an onion model or hierarchy can help us see top-down or bottom-up causality, and chart the flow of influence.

...

Each of these visual aids helps us see things we couldn’t have possibly seen through a block of text, especially when co-created with people outside our discipline. They enable us to align viewpoints, understand root causality, synthesize, generate options and even simulate design decisions within our product experiences and organizations. Quite frankly, being able to produce these artifacts is a superpower.

That superpower, however, shouldn’t go unchecked. Systems can also be consciously designed to manipulate, cheat and sometimes even harm people. This underscores our responsibility to make we have a full understanding of the chain reaction our design decisions cause beyond canvas and code.

Just like the astronauts whose new perspective of our planet engendered a new sense of responsibility for the environment, we can use whatever palette of system diagrams to shape, tug or prune systems in small but impactful increments and become catalysts for sustainable and ethical change.

How Does Life Unfold? A Landscape Metaphor Comes Into Its Own

Mike's Notes

Excellent article by Phillip Ball in a recent issue of Quanta Magazine.

Important insights from biology. Highly usable within Pipi for fitness landscapes. Could be combined with Hilbert spaces. To be worked on and added in a future minor release.

I'm fascinated. The image below of mouse embryonic development is very similar to how Pipi grows. Must be on the right track.😎

Resources

• https://www.quantamagazine.org/how-does-life-unfold-a-landscape-metaphor-comes-into-its-own-20261009/
• https://www.blog.ajabbi.com/2026/09/hilbert-spaces-and-matrix-calculations.html
• https://mitpress.mit.edu/9780262513661/evolution/
• https://doi.org/10.1038/nrg933
• https://www.quantamagazine.org/why-are-rivers-so-mathematical-20260810/
• https://www.crick.ac.uk/research/find-a-researcher/james-briscoe
• https://alleninstitute.org/person/susanne-rafelski
• https://doi.org/10.1007/BF02426835
• https://www.taylorfrancis.com/books/mono/10.4324/9781315765471/strategy-genes-waddington
• https://www.the-scientist.com/the-lac-operon-a-lesson-in-simple-gene-regulation-72357
• https://umap-learn.readthedocs.io/en/latest/how_umap_works.html
• https://biology.washington.edu/people/julie-theriot
• https://isbscience.org/people/sui-huang-md-phd/?tab=biography
• https://doi.org/10.1016/j.ydbio.2007.02.036
• https://doi.org/10.1098/rsfs.2022.0002
• https://davidrand.uk/
• https://doi.org/10.1016/j.semcdb.2025.103654
• https://www.quantamagazine.org/cells-use-bioelectricity-to-coordinate-and-make-group-decisions-20260112/
• https://doi.org/10.1371/journal.pbio.3003953
• https://en.wikipedia.org/wiki/Philip_Ball

References

• The Strategy of the Genes by C.H. Waddington. Routledge. 1957.  ISBN 9781315765471

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Last Updated

16/10/2026

How Does Life Unfold? A Landscape Metaphor Comes Into Its Own

By: Phillip Ball

Quanta: 09/01/2026

Phillip Ball: Contributing Writer.

...

C.H. Waddington’s powerful image of embryonic stem cells specializing on a rolling landscape has captivated biologists since the 1950s. Experimental data is finally confirming, and complicating, his visionary ideas.

In this update to Waddington’s image, a ball (representing a cell lineage) multiplies and specializes into cell types (represented by color) as it traverses a dynamic landscape. This animation was generated from Waddington’s original drawing using an AI tool.

Mark Belan/Quanta Magazine

By the middle of the 20th century, it had begun to seem that biology was almost solved. Charles Darwin’s theory of evolution by natural selection explained how organisms change and adapt. The modern science of genetics showed how it works at the molecular scale. Genes governing the traits of organisms get passed down from parent to offspring, and random mutations create the variations from which natural selection picks the “fittest.” Natural selection and genetics fitted together in what the biologist Julian Huxley christened “the modern synthesis” in his 1942 book. Once it was shown in 1953 that the genes are encoded by DNA, the rest of biology looked like little more than piecing together details.

But not everyone was satisfied. The British biologist Conrad Hal Waddington, known to his friends and colleagues as Wad, thought this focus on genes was all very well. But it wasn’t clear to him, or anyone else, exactly how genes shape the forms and features of organisms — how they reliably generate all the different tissues of the body, in all the right places, during embryonic development. Around the same time that Huxley unveiled the modern synthesis, Waddington presented a novel picture of how this might unfold.

He visualized the developmental process as a landscape with hills and valleys that split every so often like those in a river network. At the very top of the highest hill he imagined a ball, which represented a population of cells in their earliest embryonic form — what we now call stem or pluripotent cells, which have the potential to become any cell type. The progression of the cells towards their fates — to become, say, skin cells, muscle cells, or nerve cells — is like the ball rolling downhill through the landscape.

When the ball reaches a branching point (bifurcation), the model’s “gravity” pulls it to one track or the other: In scientific terms, it must differentiate. Through a particular sequence of such branching decisions, Waddington argued, cells specialize into their mature types, or fates, each expressing a particular set of genes. The cells are restricted to a small number of well-defined states because they are “canalized,” in Waddington’s lexicon: trapped and channeled by the sides of the valley. In this way, a limited number of cell types reliably arise from the activity of an army of genes.

The biologist Conrad H. Waddington’s prescient ideas shaped the modern understanding of gene regulation, epigenetics, and evolutionary developmental biology. He died in 1975.

Walter Stoneman, National Portrait Gallery London

Waddington’s landscape has been “remarkably useful as a conceptual scaffold,” said James Briscoe, a developmental biologist at the Francis Crick Institute in London. “The core ideas — that development is progressive, that cell states are distinct, that bifurcations represent [cell] fate decisions, that canalization implies robustness — all survive.”

“There’s so much richness to some of the concepts Waddington was trying to share,” said Susanne Rafelski, a biochemist at the Institute for Stem Cell and Regenerative Medicine in Seattle.

But the concept was more of a visual metaphor than a representation of an actual biological process. Or so it seemed. In the past several years, researchers have mapped out the real topography of cell-state spaces — in effect, Waddington’s landscapes — from experimental data collected from many thousands of cells over the course of embryonic development. Those analyses are revealing that development is not a mere readout of a genetic program, but rather a dynamic process in which communities of cells build themselves, and the very landscape they navigate, by mutual negotiation and interaction, into an organism.

This cartography of development is now helping researchers understand how identical cells in the early embryo develop into the distinct tissues and cell types of a complex organism. This matters enormously for regenerative medicine and stem-cell engineering, Briscoe said: “If you understand the landscape topology and know where the bifurcations lie, you can in principle design methods that steer cell populations to desired states with precision, rather than by trial and error.”

Waddington’s metaphor, then, could be the key to understanding and reshaping possibilities for what cells, tissues, and embryos can be.

Charting a Landscape

In the 1940s, genetics and embryology were separate sciences; not everyone even believed that genes played a significant role in development. Yet Waddington’s own experiments in embryology, and those of others, convinced him that the formation and patterning of tissues in an embryo were indeed controlled by genes. The cells of different tissues had different groups of genes activated in their chromosomes, he thought. A process of differentiation gradually specialized cells into skin cells, nerve cells, and so on.

Waddington figured that the developmental pathways — for example, the formation of the neural tube from a layer of embryonic tissue called ectoderm, which ultimately develops into the central nervous system — are inevitable once the process has begun, just as water flowing down a river valley is constrained by its surroundings. The path might meander a little, but the valley keeps the river on course. Differentiation corresponds to the branching of a valley, and the final destinations are the mature cell types of the body.

Waddington’s classic drawing of the epigenetic landscape. A ball, representing a pluripotent cell or population, is poised to roll downhill into valleys, each of which represent a specialized cell type, such as a blood cell, liver cell, or brain cell.

From The Strategy of the Genes by C.H. Waddington

Waddington visualized this as a landscape in drawings, but they had “no grounding in physical reality,” wrote Scott Gilbert, a developmental biologist at Swarthmore College in Pennsylvania, in a 1991 essay in Biology & Philosophy. They were useful schematics for thinking about the problem of development, and nothing more.

“Without mathematical content,” Briscoe said, the metaphor “could not distinguish between alternative mechanisms, make quantitative predictions, or be falsified.” As a result, he said, Waddington’s landscape “has sometimes become a cliché rather than a meaningful explanation.”

What, for example, determines the topography of the hills and valleys? In his 1957 book The Strategy of the Genes, Waddington depicted the landscape as he figured it might look from underneath: as a sheet that is tugged into shape by a network of ropes attached to pegs. The pegs represented individual genes, and the ropes their effects on development. Expressing a particular gene — turning it into its corresponding protein — is like pulling on its ropes to change the shape of the sheet.

What determines the landscape’s shape? Waddington sketched a network of pegs and ropes beneath the rolling hills; the pegs are genes, and the ropes are their interconnections. Together they shape the landscape above and represent what we now call gene regulatory networks.

From The Strategy of the Genes by C.H. Waddington

It’s rather more complex than that, Waddington realized, because the ropes are connected in a network. Any one of them might influence the landscape at several points, and any given point on the landscape might be attached to several ropes.

These hidden connections correspond to what researchers now call gene regulatory networks: interactions among genes whereby an increase in the expression of one might change, or regulate, the activity of others. Herein lies the cryptic complexity of development. A given process, such as formation of the neural tube, might be influenced by many interacting genes; there’s no simple relationship between genes (genotype) and the developmental landscape that determines form (phenotype). Gene regulatory networks mediate between them.

It makes no sense, then, to look for genes dedicated to creating specific tissues, organs, or structures in a whole organism, which is one reason Waddington’s metaphor proved useful. The landscape image was “valuable in providing an alternative to a purely gene-centric view of development,” Briscoe said. By emphasizing the landscape’s shape, it pointed “toward system-level properties that cannot be read off from any single gene.”

"Given that the channels and spheres had no physical reality, what was an embryologist supposed to do with them?" - Scott Gilbert, Swarthmore College

Gene regulation wasn’t understood when Waddington first devised his landscape and rope network. A few years later, in the early 1960s, it became clear that one gene could turn another on or off. Researchers, such as the complexity theorist Stuart Kauffman, began to construct simple mathematical models of the networks of gene interactions. But too little was then known about real gene networks to connect abstract theory to experiment. “Given that the channels and spheres had no physical reality,” Gilbert wrote, “what was an embryologist supposed to do with them?”

Answers have begun to emerge over the past two decades thanks to new experimental tools for characterizing cell states. Biologists are now finally becoming able to map experimental data to a real mathematical landscape for differentiation and show just how prescient Waddington was.

Fated Attractors

One way to define the state of a given cell in an embryo is by the expression levels of its genes. These levels can now be measured simultaneously in many cells using a technique called single-cell RNA sequencing, which supplies a snapshot of all the different RNA molecules each cell contains. An RNA molecule (or transcript) is a kind of copy of a gene used to translate it into a functional protein, and generally, more RNA transcripts means higher expression of that gene. Each cell type is characterized by particular gene-expression settings, which are inherited when a cell divides. This ensures that a liver cell will stay a liver cell as it progresses through an organism’s development process.

In principle, RNA-sequencing data can be modeled as a high-dimensional mathematical space in which each axis denotes the number of RNA transcripts of a single gene. Such a plot, with maybe thousands of dimensions to represent thousands of genes, is impossible to visualize, but it can be mathematically projected onto a space with fewer dimensions, usually just two, to produce something like the shadow of a complex 3D object.

On such a plot, called a UMAP (uniform manifold approximation and projection)(opens a new tab), different cell types appear as clusters of points — blood cells cluster together, say, as do cells of the muscle or forebrain — as they share a gene expression profile. Cell lineages follow paths through this map as development proceeds. When the data is mapped through time — in what’s known as a network flow model — it looks very much like a population of cells rolling through a Waddington-like landscape, being channeled along valleys that lead to basins that correspond to distinct cell types.

A Landscape of Embryonic Development

Mark Belan/Quanta Magazine; Modified from  with permission of the authors

We should be wary of supposing, though, that this gene-expression map is literally Waddington’s landscape. Julie Theriot, a biophysicist at the University of Washington, cautioned that single-cell RNA sequencing is not the only or even the best way to characterize cell states. The technique was “mind-blowing” at first, she said, but RNA transcripts don’t map perfectly to gene expression. Plus, when researchers collapse the data into low-dimensional maps, they often do so in ways that reflect prior assumptions about how cells cluster into different types.

Still, it’s clear that, as Waddington supposed, cells undergo changes in state, defined by which genes are active or which proteins are made, as they mature and develop, and that these changes have a trajectory through time that carries them toward a limited number of final states. So how is that landscape of possibilities shaped? What governs the paths it offers?

"We think this reflects something real about how gene regulatory networks are organized." - James Briscoe, Francis Crick Institute

In 2007, the biologist Sui Huang, now at the Institute for Systems Biology in Seattle, described a bifurcation in cell state of the sort proposed by Waddington. He found that blood-forming cells called myeloid progenitor cells differentiate into two specialized types, setting them on course to become red or white blood cells, in a process dominated by two proteins, GATA1 and PU.1. This landscape of cell differentiation can thus be represented with just two values: the concentrations of these two proteins. More GATA1 promotes the formation of red blood cells, while more PU.1 sends the cell toward a white-blood-cell fate.

Huang and his colleagues mapped out the landscape using data from RNA-sequencing experiments and theoretical calculations. At first there is just a single basin, corresponding to the pool of myeloid progenitor cells. But as differentiation proceeds, this basin becomes less stable — less deep — relative to two new adjacent basins, which correspond to the red (high GATA1) and white (high PU.1) blood-cell states. Think of it like a sheet suspended horizontally from its corners, with water pooled in the middle. If you push up on the pool from below, the water flows out sideways toward new, lower pools on either side. These basins are what mathematicians call attractors: states toward which the cells are inevitably drawn, like water following gravity across a landscape.

The biologist Sui Huang has found that blood cell differentiation can be mapped like a real epigenetic landscape, with changes in gene activity altering the shape of the landscape, resulting in an output: red or white blood cell.

Allison Kudla

Researchers have now documented many cell-fate decisions(opens a new tab) that can be described in this way: as changes in gene activity that alter the shape of a landscape. The changes produce new attractors that correspond to stable cell types in the low-lying valleys; unstable gene-activity configurations constitute the high ground.

Briscoe is part of a research group that has pursued the landscape idea using the language of dynamical systems theory, which explores how systems described by sets of interacting variables evolve. “Our work starts from Waddington’s metaphor,” said David Rand), a mathematician at the University of Warwick in the United Kingdom, who collaborates with Briscoe. It’s more than a convenient image, he said: It “reveals something [fundamental] about the structure of developmental systems.”

Combining experiments and theory, they too have found that a small number of key genes drive cell-fate decisions. The rest of the network enacts the decision, as other genes are activated or suppressed farther downstream, but isn’t active in it. “We think this reflects something real about how gene regulatory networks are organized,” Briscoe said.

This makes sense from an evolutionary perspective. For any multicellular organism, the various cell types must be reliably generated and stable. Gene regulatory networks that have deep, well-separated attractors will be selected for, Briscoe said.

These deep wells represent canalization of cell-fate decisions in the way Waddington supposed. The molecular world is noisy, filled with random fluctuations. Two cells of the same type will inevitably have slightly different levels of gene expression at any moment. In the landscape picture, they don’t have to occupy the exact same spot. But so long as a group of cells is in the same basin, they will all follow the same basic trajectory.

“If fate decisions were highly sensitive to any molecular fluctuation,” Briscoe said, “development would not be robust.”

Forces of Nature

Waddington’s alluringly intuitive view left some open questions. What does the height of the landscape actually represent? And what’s the developmental equivalent of time passing as his balls roll down the slopes?

Briscoe and Rand’s dynamical-systems view provides some answers. It suggests that the landscape’s height reflects the stability of the cells’ states, with the least stable states on the highest peaks and the most stable in the lowest valleys. Time, meanwhile, is not literal time, but a kind of “pseudo-time” that reflects how the gene-expression profiles of all the cells change. In theory, all the cells can be ordered along a single pseudo-time axis so that the greater the change in a given cell’s state, the farther along the axis it is. But these changes won’t all be in sync, so there’s no ticking clock that applies to them all at any instant — hence, pseudo-time.

The biologist James Briscoe has found that a small number of key genes determine whether a cell specializes into a certain cell type, such as a brain or liver cell. Those genes then activate or suppress other genes farther downstream in the network.

Dave Guttridge/The Photo Unit

The trouble is that this notion of time doesn’t always work, Rand said. It fits when cells are crossing over high, unstable points in the landscape. But when they are in a low-lying attractor basin, their expression profiles don’t settle into a single, stable pattern but rather seem to evolve more randomly than systematically, as if “time” meanders.

And what plays the role of gravity in this picture? What is the force that moves cells along their development pathway? “In developmental biology, gravity is [equivalent to] the inevitable progression of differentiation in multi-celled organisms, starting from a small number of cell types,” Rafelski said. This process seems to have an inexorable direction and impetus. “Most of the time, cells follow a predictable order in which they differentiate,” Theriot said. But that order is not programmed into the genome(opens a new tab); after all, the cells in an organism all have the same genome. Rather, the cell-fate decisions are made collectively, as cells interact with and communicate with one another through chemical signals that are read at the cell surface, and through the tugging and squeezing on cell membranes that takes place as tissues grow and fold.

“In development, there’s always feedback,” Theriot said, “and things [in the cell] can adjust depending on inputs from the outside.”

"It is as though the landscape is constantly being reshaped by underlying tectonic forces".

So while Waddington’s landscape looked like a static territory waiting to be traversed, the real landscape changes as cells develop. “In our framework, the landscape is a feature of each cell and is not fixed,” Rand said. “Attractors appear and disappear through bifurcations, and the routes connecting them change.” It is as though the landscape is constantly being reshaped by underlying tectonic forces. Waddington’s rope network, you might say, is itself in constant motion, pulling the sheet this way and that.

In this respect, Rand said, Waddington’s picture was incomplete. It was never really a kind of universal gravity that made the ball roll; rather, cell lineages all move through a constantly shifting landscape in the process of life’s unfolding.

In such ways, modern data has both confirmed and complicated Waddington’s powerful metaphor. Combined theoretical and experimental approaches have helped to elucidate the complex but vital process through which the cells of multicellular organisms like us navigate a path from all-purpose, pluripotent stem cells to well-defined and specific tissues. Briscoe, for instance, has found that in neural-tube patterning there are two ways of reaching the same place(opens a new tab) in the landscape — a finding that reconciled previously contradictory observations. His team could not have made sense of their results without the landscape framework, he said.

Huang is now applying the landscape picture to the development of cancers. The traditional perspective is that cancer cells are malfunctioning, generally because gene mutations have damaged cell division so that it proceeds unchecked. It’s a picture that sees cancer as an aberrant state. But Huang regards it as just another attractor in the landscape — albeit a potentially catastrophic one — that can be all too easily accessed by many different routes. In this view, becoming cancerous is just one of the possible things that our cells do: an inevitable consequence, you might say, of being a multi-tissue organism.

This doesn’t mean, however, that there’s nothing we can do about it. Once we view cancer as an attractor state, a new option becomes possible: to guide those cells back out of the cancer basin and into another that is more benign — one where the cells do nothing, undergo spontaneous cell death, or even regain a healthy state. “Rather than just saying we need to kill them all,” Theriot said, “the idea is: Can we drive them to differentiate?”

Such control of landscape trajectories might also be used for tissue regeneration, or for transforming one cell type into another — for example, to regrow damaged nerves. There could be uses in agriculture as well, for selectively enhancing edible parts of plants, Theriot said.

Briscoe said that “the landscape framework provides a rational basis” for this kind of developmental design, opening up the promise of a “generative” biology in which tissues and possibly organisms can be sculpted to order. In that case, Waddington’s landscape model is far more than metaphor — it could be an atlas of a hitherto uncharted terrain.

Inside interoception: The hidden sense of how you feel inside

Mike's Notes

I discovered this article in MIT Technology Review by reading a recent note about AI by John F. Sowa on the Ontolog Forum.

"The guys from Anthropic have ZERO understanding of the complexity and power of the human brain-body system.   For a better perspective, I recommend a recent article from MIT Technology Review.  It shows that our conscious information is a tiny fraction of the immense amount that the brain is processing every second.   The languages we speak express a tiny fraction of what is conscious.  But the part outside of consciousness is immensely larger .  

I copied a few paragraphs that emphasize the immense amount of non-linguistic information that LLMs (Large Language Models) ignore. ..." - John F. Sowa 

Resources

• https://www.technologyreview.com/2026/06/12/1138833/inside-interoception-brain-body/
• https://groups.google.com/g/ontolog-forum/c/A1i2xF27nqw

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Last Updated

04/10/2026

Inside interoception: The hidden sense of how you feel inside

By: Katherine W. Isaacs 

MIT Technical Review:12/05/2026

Katherine W. Isaacs: Katherine W. Isaacs is a writer and senior lecturer at the MIT Sloan School of Management. Her teaching and research focus on the intersection of psychology, technology, and innovation. Originally trained as a biologist and later as a social psychologist, she is currently working on a book called Gut Feel, about intuition, interoception, and embodied decision-making.

...

Researchers are decoding how signals move between body and brain, with implications for how we understand and treat conditions from obesity to anxiety.

Your brain lives in the dark space of your skull. Yet it knows when the wind lifts the hairs on your skin, when your heart is racing, when your gut tightens with fear.

It’s also, right now, predicting what you’ll read next as your eyes move across this page. It’s picking up signals that help it make sense of what’s happening around you and prepare you to act if you need to stay safe. You aren’t usually aware that your brain is doing all that.

Our senses take in information at a staggering rate—roughly 11 million bits flood in every second from our skin, eyes, ears, and more. That’s nearly three paperback novels’ worth of data every second. Only a sliver reaches our conscious awareness. Researchers estimate that our conscious minds can process roughly 10 to 60 bits of information per second, about the rate at which you’re reading this sentence. That’s a ratio of about one conscious bit to hundreds of thousands of unconscious bits.

And that’s a mercy. As Moriah Thomason, a neuroscientist at NYU Langone, says, “Thank goodness we’re built like this. There’s a layer of what we have access to in conscious awareness. And then we have a right-under-the-surface amount. There is only a certain amount we are meant to ‘hold in mind’ in order to function successfully.” 

What you are aware of: Your stomach growling when you’re hungry. Your palms sweating before you speak in public. The breath you just took, if you pay attention to it. Even your heartbeat, which some people can sense from the inside without feeling their pulse in their wrist.

Scientists have a word for how we sense ourselves from the inside: interoception. 

The term was coined in 1906 by the British neurophysiologist Charles Sherrington. For most of the 20th century it remained largely confined to textbooks. Today, thanks to a 2021 Nobel Prize and new tools that can map the interoceptive system across the body, the study of this facility is suddenly quite hot. As researchers decode how signals move between body and brain, a clearer picture is starting to take shape—with implications for how we understand and treat conditions from obesity to chronic pain to anxiety.

The field began to take off in the 1990s. In 1994, the neurologist Antonio Damasio published a book with a pointed title: Descartes’ Error. He challenged the historical separation of thinking and feeling, arguing that our ability to choose and act is driven by feelings, and those feelings in turn are shaped by the body’s signals, such as your gut clenching or your skin going clammy. When we lose that connection between feeling and thinking, as one of Damasio’s patients did after surgery to treat a brain tumor, we may still be able to reason with perfect logic about the pros and cons of traveling on a Tuesday or a Wednesday. But without the emotional signals that help us predict what a choice will feel like, our reason spins and circles, and we cannot decide.

A contemporary of Damasio’s, the neuroscientist Bud Craig, spent his career asking one question: How do you feel? He charted how the brain builds an inner map of the body and updates it in real time every moment you are alive.

Think of the captain’s bridge on the USS Enterprise, where a live map displays the status of the ship’s critical systems: oxygen levels, energy availability, hull integrity, shield strength. Another set of indicators senses things outside the ship: asteroid belts, enemy ships, radiation, life signs, and spatial anomalies not yet understood.

Your brain, only about the size of your two fists pressed together, creates a map like this for your entire body, along with a map of the outside world, from data streaming in through your five senses. Together, they feed into your brain’s working model of you in the world, now and across time—where you are, who you are, your expectations for what’s about to happen (based on everything you know), and what all that means for you.

When someone asks “How are you doing?” we consult our maps and report back on our status. We might say we’re happy, depleted, anxious, or energetic. These feelings are always a braid of emotional and physical sensations. They’re what your interoceptive navigational system serves up to your awareness when you sense yourself from the inside.

As we grow up, we learn to interpret what these sensations mean—interpretations that, in turn, can alter our physiology, emotions, and behavior. Research by the psychologist Alia Crum shows that people who embrace a “stress is enhancing” mindset produce more growth hormones than people who have a “stress is debilitating” mindset. They also experience more positive emotions and greater cognitive flexibility.

Language also matters. We learn words for the textures of our feelings—words that then shape how we feel and act. The psychologist Marc Brackett points out that people low in "emotional granularity"—the ability to distinguish between closely related feelings—react more impulsively under stress and are less able to find meaning in difficult experiences.But mindsets and emotional intelligence are malleable. We can learn that “anxious” is different from “terrified,” and we can even reframe how we interpret our body’s sensations. Instead of thinking of the butterflies in our bellies as annoying, we can welcome them as our body’s way of preparing us for a peak performance.

Research by the neuroscientist Lisa Feldman Barrett, who discovered and named emotional granularity, suggests that it is not about describing or labeling emotions. It's about how our brains construct emotions with more or less specificity, often outside of awareness.

Scientists have long understood that the interoceptive information informing these lived experiences travels via two major systems: nerves and humors (blood and lymph). Now they’re actively studying a third system—the “interstitium,” a network of fluid-filled spaces woven throughout the body’s connective fascia that may also play a role in communication.

But until recently, scientific understanding of this interoceptive system looked like a high-level schematic that left out vital details—how information travels from the outside environment in, how it moves from your body to your brain, and how it is integrated and interpreted within your brain. Researchers are now racing to explore what the neuroscientist Catherine Tallon-Baudry calls this “new continent of awareness.”

The wandering highway

One of the most active areas of research centers on the vagus nerve, the main component of the parasympathetic nervous system and an information highway carrying news from your organs up to your brain and back down to your body. The vagus has become a celebrity nerve, ubiquitous in wellness podcasts and trauma therapy. “Tone your vagus nerve.” “Activate your parasympathetic system.” The language suggests a single thing you can target, like a muscle. The reality, as Steve Liberles at Harvard Medical School is discovering, is far more interesting.

Liberles has spent most of his career mapping what he calls “the great wide unknown” of one of our largest and longest nerves. He speaks the way he works—methodically, without overselling. But the questions driving him are huge. How do we sense our body’s inner state? What information flows through which channels? And how does the brain decide what to do with it?

“When I’m nervous giving a talk in front of a thousand people,” he says, “my heart might race. I might get butterflies in my stomach. I might get goosebumps on my skin.” We all know what he’s talking about.

“It’s bizarre,” he muses. “Your brain has to send a signal to the gut, and then the gut back to the brain, to tell you you’re nervous?” He pauses. “This just shows there is this intimate connectivity between the brain and the body that’s real.”

The vagus is often called the calming nerve, because it controls “rest and digest” functions that quiet our body after the sympathetic nervous system revs us up with “fight or flight” impulses to handle danger or stress. 

But it is also doing something else: It’s listening to us inside. Anatomists have known for over a century that roughly 80% of its fibers carry information upward, from body to brain. Think of it as a two-lane highway with far more traffic headed north. What scientists are just beginning to understand in detail is what those signals are saying. 

Liberles is decoding the vagus with molecular precision and finding that its messaging system is unexpectedly diverse. So far, his research has uncovered dozens of types of vagus nerve cells, each wired to a specific organ. Team Red relays information about the heart; Team Blue, the gut.

Within those teams, each courier has a unique job that’s different from those all its teammates perform. Liberles found 10 types in the lungs alone. Until then, only one lung reflex had ever been identified, in 1868. One nerve courier carries information about breathing rate; another the stretch of your lungs; yet another information about airway threats, like food going down the wrong pipe.

“It’s super exciting to think about what each of these neurons is doing,” he told me in a conversation last fall, a flash of intensity breaking through the calm. “Where does it go in the body? What is it sensing? What is it controlling?”

The doors of the cell

Liberles is mapping the vagus information highways. But highways need on-ramps for signals to enter. For years, one of neurobiology’s biggest mysteries was the molecular on-ramp for our sense of touch.

Somewhere, something in our bodies was converting physical force into an electrical signal that the nervous system could understand. But no one knew how. 

Solving that mystery required a scientist willing to trust a hunch when the data couldn’t show the way. 

Ardem Patapoutian grew up in Lebanon and fled the country’s civil war at 18, landing in Los Angeles, where he delivered pizzas and wrote horoscopes for a local newspaper before falling in love with science at UCLA.

In the 1990s, as a postdoc at the University of California, San Francisco, he became fascinated with our sense of touch—the last of the five major senses not yet understood at the molecular level. The lung stretch signal that Liberles’s vagus neurons carry to the brain? No one had ever figured out how that signal began.

“How do you feel the embrace of a loved one? How do your fingers distinguish one texture of hair from another?” Patapoutian invites us to wonder in his 2021 Nobel Prize lecture. The problem: Most cellular communication works through chemistry. But mechanical force offers no molecule to bind. How does the body translate physical pressure into the electrochemical language that neurons speak?

Scientists knew that the answer had to be an ion channel—a protein gate embedded in cell membranes that opens to let electrically charged particles into the cell. But tracking down the one responsible for touch turned out to be absurdly difficult. Ion channels are a hundred thousandth the size of a cell, invisible to ordinary microscopes. Worse, they don’t resemble each other. You can’t recognize one by its shape or its sequence of amino acids. Even with one right in front of you, nothing would tell you it was there.

At Scripps, where he works now, Patapoutian decided to try an unusual approach. He’d try to find cells that showed sensitivity to touch and destroy their internal genetic blueprint one gene at a time—hunting for the move that would make the cell go numb. It was tedious, expensive, and possibly a dead end. “A lot of people made fun of us,” he says.

Two years in, Patapoutian’s collaborator Bertrand Coste had burned through half his postdoctoral appointment with no results. Patapoutian said: Another 30 genes, and then we decide whether to continue.

What kept them going, Patapoutian told me, was informed intuition. “As you gain more experience, you have this sense of what’s going to work, what’s not going to work. Sometimes the data cannot answer the question of when to stop or when to continue. There has to be another process. If you start trusting it, it gives you an avenue to continue.”

Coste knocked out candidate gene 72. Flatline. The cell had gone numb.

They’d found it—the mechanism behind something you feel every day.

They named the protein they identified PIEZO, from the Greek piezi, meaning pressure. There are two variations, PIEZO1 and PIEZO2, each responsible for sensing different kinds of pressure in the body. They’re elegant in their design—over 2,500 amino acids folded into a three-bladed propeller-shaped gate embedded in cell membranes. When pressure stretches the membrane, the gate opens and electrically charged ions flood through, translating physical pressure into an electrical signal that the brain can understand—all within milliseconds.

Patapoutian calls scientific discovery a dream that survives reality. He won the Nobel Prize in medicine in 2021 for his discovery of PIEZO, sharing the award with David Julius of UCSF for his work on how cells sense temperature. Now researchers are finding PIEZO proteins everywhere—skin, organs, blood vessels, and even red blood cells, where they help the cells squeeze through narrow capillaries. They’re how your brain knows where your hand is in space without looking at it, a sense called proprioception. They’re in plants too, enabling roots to sense pressure as they push down into the earth.

PIEZO was just the beginning. With a $14.5 million grant from the US National Institutes of Health, Patapoutian and his collaborators are now mapping the body’s entire interoceptive system—as many internal senses as he can find, he says.

Patapoutian has translated his discovery into a unique form of public outreach. At scientific conferences, he sometimes rolls up his sleeve mid-lecture to reveal half his arm covered in ink—a gigantic PIEZO protein in exquisite anatomical detail, its blades spreading across his biceps. Then he flexes. The tattoo flexes with him, the structure bending exactly as the real protein does when pressure opens the gate.

“At a pub or a party,” he explains, smiling, “how else would I demonstrate this beautiful structure?”

Orchestrating the field

Steve Liberles is mapping a major interoception highway. Ardem Patapoutian discovered the gates of touch. Meanwhile, Wen Chen at the National Institutes of Health is pulling the field together, putting neuroscientists, immunologists, physiologists, and clinicians into the same room. The demand, she says, has been enormous.

She tested her pitch at a dinner party with NIH colleagues a few years ago. You’re hungry right now—that’s interoception. You’re thirsty—that’s interoception. Heads nodded as she pointed around the table.

“We can’t have just the brain or just the body,” she told me. “We need to look at the whole person.”

In 2018 she organized a symposium on interoception where Liberles was one of the invitees, along with researchers and practitioners of meditation and yoga. “It was not their thing,” she says, laughing as she recalls how uncomfortable some of the researchers looked. But the practitioners were excited to finally meet scientists who were studying the inner mechanisms of what they did.

That was followed by a series of NIH workshops on interoception that spanned topics from basic science to clinical practice. Patapoutian was the keynote speaker for the first one. 

The NIH began funding scientists to chart the neural circuits of interoception and bringing them together to talk about their findings. Partway through one of these meetings, the equipment failed for an hour. More than 1,000 people stayed online, waiting for it to come back.

“We were shocked at the turnout,” she says. “There was much bigger interest than we could have imagined.”

Chen is now building infrastructure to match the demand: a formal community, funding mechanisms, a venue where cardiologists and neuroscientists and clinicians can all find each other. And she’s redefining the field as she goes; interoception is not a one-way signal from body to brain but a continuous two-way communication system, each direction shaping the other in real time. 

Liberles’s nervousness on stage is that two-way loop in action. Signals from his racing heart and belly butterflies travel up to the brain, which weaves them into an interpretation: This is anxiety, and this is what to do to handle it. His actions produce fresh signals that the brain reads in light of its ongoing predictions about what will happen next. In the body-brain communication loop, each player constantly updates the other.

I asked Wen what her work on interoception might mean for another inner sense: intuition. “People talk about ‘gut feelings,’” I said. “How does that relate to interoception?”

“Intuition might be the bridge where interoception moves from unconscious processing to conscious awareness,” she answered. “If that’s true, then intuition is not magic. It’s physiology.”

But it depends on how we read the signals. Intuition is like pain. It tells you something, but it’s not always clear what. “Perhaps we can treat intuition as a source of data,” she says. “Meaningful, but probably not complete.”

“Maybe we can be grounded in both—in feeling and fact.”

Which raises a more personal question: What do you do with the signals your body is sending?

One avenue for exploration is therapeutic intervention—both pharmacological and neural stimulation. Vagal nerve stimulation has treated epilepsy and depression for four decades, but as Liberles puts it, it’s like pressing all the keys on the piano to hit one note. Weight-loss drugs like Ozempic act in part through vagal pathways but can cause nausea as a side effect, because the targeting isn’t precise enough. Map the body’s circuits with enough accuracy and you might hit the note you actually want.

Another area of active research is psychological and behavioral—teaching people how to detect and even shape interoceptive signals. Low interoceptive awareness is linked to mental-health disorders and stress-related physical conditions. But like emotional intelligence, it’s not fixed. Researchers are finding that people can boost their body awareness by, for example, learning to detect their heartbeats from the inside—now a common measure of interoceptive awareness. Other interventions focus on body-based therapies and conscious activation of the parasympathetic “rest and digest” system to improve emotional and physical well-being. The placebo effect is another example of the mind acting on the body through expectation alone.

The signals we once dismissed as vague feelings—when your gut tightens before you know why, when your body says yes or no before your mind catches up—those are real. How we interpret them and whether we act on them is another frontier.

It’s clear that gut feelings play a role in scientific research, especially when the path forward looks foggy. Patapoutian’s informed intuition kept him and his colleagues going long enough to find PIEZO, a reminder that major discoveries often start with a hunch that is later tested against evidence. Chen puts it well: Maybe we can be grounded in both feeling and fact.

Update

This story was updated to include information on Lisa Feldman Barrett's work.

...

Katherine W. Isaacs is a writer and senior lecturer at the MIT Sloan School of Management. Her teaching and research focus on the intersection of psychology, technology, and innovation. Originally trained as a biologist and later as a social psychologist, she is currently working on a book called Gut Feel, about intuition, interoception, and embodied decision-making.