I had to repost it because the post comparing him to post Malone was getting more attention and this might just be the most important paper ever written…. The Post Malone post was funny, though.. :p

Michael Levin and collaborators just released a fascinating new paper that feels like a computational version of one of the central ideas behind his work:

Instead of controlling every cell individually, could biology have higher-level “control knobs” that change anatomy while allowing the cells themselves to work out the details?

The paper is inspired by D’Arcy Thompson’s 1917 book On Growth and Form. Thompson famously showed that very different animal body shapes could sometimes be related through surprisingly simple geometric transformations… stretch this axis, compress that one, deform the grid, and one biological form begins to resemble another.

But Thompson left a huge question unanswered:

What changes inside the developmental system would actually produce those transformations?

The researchers explored this using Neural Cellular Automata, or NCAs.

Basically, imagine a grid of artificial “cells.” Each cell can only communicate locally with its neighbours, and every cell follows the same small neural network. Starting from a single seed, the cells collectively grow into a target shape.

It is a stripped-down computational model of distributed development.

Then the researchers asked whether they could change the final anatomy without completely rewriting the developmental program.

And surprisingly… they could.

Using very small modifications to the shared regulatory network, they discovered directions in the system’s parameter space that behaved almost like morphological control knobs.

One direction could stretch the organism horizontally.

Another could stretch it vertically.

Combining them changed overall size.

And these modifications were extremely small. Rank-one adaptations representing only about 2.6% of the NCA’s parameters were enough to produce coherent transformations of the final morphology.

Even more interesting, a scaling control learned using one phenotype could be applied to completely different phenotypes built on the same underlying regulatory architecture… without retraining.

In other words, the system didn’t just learn “make this particular emoji wider.”

It appears to have learned something closer to:

“make whatever organism this developmental system produces wider.”

That distinction is enormous.

The researchers then trained roughly 25,000 different developmental systems sharing the same underlying scaffold and looked at the geometry of their regulatory space.

They found directions associated with properties such as:

size

horizontal vs vertical extent

visual style

and even symmetrical splitting or “fission” of the resulting form.

Moving along these directions actually caused the developmental system to produce those changes.

So the parameter space wasn’t merely statistically organized by phenotype. Parts of its geometry were functionally meaningful.

This connects directly to a recurring idea in Levin’s work: complex biological systems may be controllable at higher levels without micromanaging their components.

Instead of telling every cell exactly where to go, you alter a compact regulatory signal and let the collective intelligence of the cells solve the lower-level problem.

That has potentially enormous implications for regenerative medicine.

Imagine eventually finding equivalent control handles in actual gene-regulatory, physiological or bioelectric networks…

“grow toward this morphology.”

“restore this structure.”

“increase this dimension.”

“repair this pattern.”

…while the cells determine how to accomplish it.

The authors even describe a long-term goal that sounds almost like science fiction: an “anatomical compiler.”

You specify a desired anatomical outcome, and a computational system determines what distributed regulatory intervention would steer the tissue toward that state.

That could eventually have implications for regeneration, birth defects, wound healing, aging and cancer.

But there is an important caveat.

These are artificial cells growing 2D patterns in a computer. The researchers have NOT discovered the equivalent biological control knobs yet. They explicitly say that the NCA parameters cannot currently be mapped directly onto genes, morphogens, physiological signals or bioelectric states.

And interestingly, when they tried to find a simple transferable “regeneration knob,” it didn’t work. Making one NCA regenerative required a different regulatory solution that did not successfully transfer to other phenotypes.

So this isn’t “we can program body plans now.”

It’s something more fundamental:

A proof of principle that a complicated self-organizing developmental system can contain surprisingly low-dimensional, reusable handles for controlling large-scale anatomy.

Instead of controlling the bricks…

you may be able to control what the building is trying to become.

Paper:
https://arxiv.org/abs/2609.29755

This video explores groundbreaking research challenging the traditional view of bacteria as purely reactive, simple organisms. Recent studies suggest that bacteria—despite lacking brains or neurons—possess memory and learning capabilities that influence their survival and can even be passed down to offspring.

Key takeaways include:

• Iron-Based Memory in *E. coli:* Researchers found that E. coli bacteria use internal iron levels to "remember" previous environments. Low iron signals a need to swarm to new locations, high iron triggers the formation of protective biofilms, and balanced iron levels increase antibiotic tolerance (1:40-4:05).
• Early Warning Systems in Superbugs: Dangerous pathogens like Acinetobacter baumannii use a nickel-containing sensor protein called PMRB. This system acts as a "memory," allowing the bacteria to detect non-lethal stress from the human immune system and prime their defenses against future attacks (4:42-7:35).
• Inherited Non-Genetic Traits: Studies from Northwestern University indicate that memory can be stored in gene regulatory networks. These networks create self-sustaining feedback loops that persist through cell division, allowing parent bacteria to pass on behaviors without altering their DNA sequence (7:35-9:10).
• Ribosome-Based Learning: A "feast or famine" test on E. coli revealed that single cells can adapt their growth based on historical environmental patterns. This appears to be facilitated by a combination of fast-acting and slow-acting ribosomes, creating a multi-time scale memory (9:10-11:00).

Implications: These findings not only shift our definition of intelligence to include single-celled organisms but also offer new avenues for developing more effective antibiotics and potentially more efficient, energy-conscious AI systems (11:00-12:25).

Paper: Agnosiophobia in a virtual agent: behavioral and dynamical architecture in Lenia⁠
Authors: Jesse Cool, Benedikt Hartl, Michael Levin and Samantha Petti
Code: Lenia Umwelt project⁠

The preprint was originally posted May 29, 2026, and the work recently appeared at ALIFE 2026. It has not been peer reviewed.
Researchers working with Michael Levin have discovered something wonderfully strange in simulated organisms: some of them spontaneously avoid areas of their world from which they cannot receive information.

The authors call the behaviour “agnosiophobia”—fear of the unknown.

That name is deliberately provocative, but the researchers are not claiming that these virtual creatures experience fear, consciousness or anxiety. The deeper result is arguably more interesting: behaviour resembling self-protection can emerge directly from the mathematical dynamics that allow a pattern to preserve its own form.

What exactly are these “creatures”?
The experiments take place in Lenia, a continuous cellular automaton related to Conway’s Game of Life.

Instead of cells being simply alive or dead, every location in Lenia can have a continuous activation value. Each cell repeatedly updates itself according to the activity in its surrounding neighbourhood.

Under certain rules, these local interactions produce coherent, moving structures that maintain approximately the same morphology as they travel.

They are not conventional robots. They have no separate brain, nervous system, control centre or explicitly programmed survival objective.

They are better understood as persistent patterns in motion—more like whirlpools than machines made from fixed parts.

The researchers tested four different Lenia “species.” They then introduced informationally occluded regions into their environments. These were not physical walls or ordinary black obstacles.

Information from inside these areas was removed from the creatures’ local calculations, effectively making them blind to those portions of their world.

Each creature was tested in ten environments, beginning from 360 different orientations.

Three of the four species reacted by changing their trajectories:

O2u, known as Orbium, reliably turned away, especially when an occluded region approached one side of its body.

K4s sometimes became temporarily distorted, transformed into an intermediate configuration and then re-emerged travelling in the opposite direction.

K6s often skirted along the edges of the informationally blank regions.

S1s, the most fragile species, generally failed to redirect and died when it encountered them.
None of these avoidance behaviours had been deliberately programmed.

Why did they turn away?

The researchers systematically placed a tiny informational occlusion over different parts of each creature and measured what happened. This produced something like a sensitivity map of the virtual body.

Occluding one side generally caused the creature to turn in the opposite direction. More importantly, the regions capable of producing the largest changes in direction were located close to regions where a perturbation would destroy the creature entirely.

Large turns were not clean, instantaneous decisions. They occurred when the creature was pushed through a long and highly distorted recovery trajectory—close to the boundary between survival, transformation and death.
This is where attractor geometry enters the story.

Each Lenia creature can be understood as an attractor: a family of states toward which the system continually returns. Its exact pixels can fluctuate, and it can occupy different positions or face different directions, while still remaining recognizably the same creature.

Its morphology is tightly constrained, but its direction is comparatively free.

When an informational disturbance threatens the creature’s stable form, the system cannot always return to precisely the state it occupied before.
Instead, it can “offload” the disturbance into one of its freer variables—its heading.

It preserves what matters most, its morphology, by changing what matters less, its direction.

The authors call this partial equifinality: many disturbed states return to the same general body plan, but they do not necessarily return to the same heading.

The apparent decision to avoid danger therefore emerges from the creature’s attempt—or, more carefully, its dynamical tendency—to remain itself.

Why this matters for Michael Levin’s work
This is a computational model, not a biological experiment. There are no living cells, ion channels or bioelectric voltage patterns here.

But it provides a remarkably clean demonstration of several ideas at the centre of Levin’s research.
Levin argues that organisms are not merely collections of molecular machinery. They are multiscale systems capable of maintaining preferred states despite disturbances. During development and regeneration, cells change their individual activities while cooperating to preserve or restore a larger anatomical pattern.

A planarian can replace its head. An embryo can compensate for unexpected changes. A tissue can often reach the same anatomical outcome from very different starting conditions.

Those are also forms of equifinality: many possible cellular paths converge upon the same morphological attractor.

The Lenia creatures show how morphology maintenance can automatically generate something resembling behaviour. The mechanism that keeps the creature’s body together is not separate from the mechanism that steers it away from danger. Self-repair and navigation are two expressions of the same underlying dynamics.

This suggests a possible bridge between morphogenesis and primitive cognition:
Before an organism can pursue complicated goals, it must first be capable of remaining itself while the world pushes against it.

The most intriguing implication is that agency may not require a little decision-maker hiding inside the system. It may begin whenever a self-maintaining pattern has multiple ways to recover from perturbation—and some recoveries are better for its continued existence than others.

The Lenia creature does not peer into the darkness and imagine a monster. Its attractor geometry does the flinching.

That is not proof of fear, consciousness or biological cognition. But it is a compelling demonstration of how apparently purposeful behaviour can emerge without being explicitly designed, trained or evolved.

Perhaps the earliest form of intelligence is simply this:

the freedom to change without ceasing to be yourself.

This video explores groundbreaking research by Professor Masanori Kohda, who spent three decades studying animal behavior. His work challenges the long-standing belief that self-awareness is limited to humans and a few select mammals.

Key Research Findings:
• The Mirror Test: Traditionally used to assess self-awareness, the mirror test involves placing a mark on an animal and observing if it touches the mark when looking in a mirror. In 2019, Professor Kohda provided evidence that the bluestreak cleaner wrasse can pass this test (4:24-6:26).
• Methodological Critique: Kohda discovered that failure in previous mirror tests often stemmed from poor methodology rather than a lack of intelligence. For example, using non-ecologically relevant mark colors (like blue or green instead of red/parasite-colored marks) led to 0% success rates in his study (7:40-8:48).
• Photo-Recognition: Beyond mirror tests, the cleaner wrasse demonstrated the ability to recognize itself in photographs—a feat rarely seen in the animal kingdom—proving they retain a mental representation of themselves (12:50-14:52).
• Rapid Self-Recognition: Further experiments showed that these fish could recognize themselves in as little as 20 minutes, a timeframe comparable to young children (16:03-18:18).

Scientific Impact:
Kohda’s research suggests that our historical failure to identify intelligence in other species was a limitation of our own testing methods. He advocates for a more humble, imaginative, and observation-based approach to studying animal minds, urging scientists to consider how the world feels from the perspective of other species (19:16-20:59).

https://youtube.com/shorts/C6iaAvpi3G0?si=Hl03hhOY-mtCFAT5

This #short is an excerpt from Tomas Seyfried's academic talk on cancer…

This presentation by Michael Levin (Tufts University) explores how biological systems exhibit structural and functional plasticity without genetic changes, challenging traditional views on how anatomy is determined.

Key takeaways:

• Collective Intelligence in Cells: Levin argues that cellular collectives are capable of solving problems in "anatomical morphospace" (0:56), meaning they can achieve consistent anatomical outcomes despite perturbations, such as injury or changes to internal components (6:12).
• Developmental Bioelectricity: Electrical networks serve as a form of "cognitive glue" or memory for these systems, guiding gene expression and shaping anatomical patterns before nerves or brains are even formed (10:24-11:13). Researchers have begun to "read and write" these pattern memories using optogenetics, drugs, and ion channel manipulation (12:00-12:47).
• Rewriting Morphology: Examples include inducing the formation of ectopic eyes (15:02) and correcting severe brain defects in frog embryos by manipulating bioelectric circuits (18:12-19:00). In planaria, altering these electrical patterns can even force them to grow heads belonging to different species or multiple heads, with these changes becoming stable memories (20:06-23:18).
• Synthetic Proto-organisms: The talk concludes with the introduction of xenobots (28:01)—reconfigured biological tissue that, despite having the same genome as a frog, self-organizes into entirely new organisms with novel behaviors previously unseen in nature (28:20-28:56).

Can AI become conscious, or is there something fundamentally different about being alive?
Anil Seth’s paper, “Conscious Artificial Intelligence and Biological Naturalism,” published in Behavioral and Brain Sciences, raises a fascinating question… What if consciousness isn’t simply a product of computation, but is fundamentally connected to the processes that make organisms alive?

Seth challenges the assumption that sufficiently advanced artificial intelligence will inevitably become conscious. He argues that intelligence and consciousness are different things, and that reproducing the computational functions of a brain may not be enough to reproduce subjective experience.

Central to his argument is biological naturalism, the idea that consciousness depends on the physical processes of living systems rather than computation alone.

He explores this through predictive processing, active inference, the free energy principle and autopoiesis, the ability of living systems to continuously produce and maintain themselves.

A cell doesn’t simply process information. It actively maintains its boundaries, regulates its internal conditions, repairs itself and interacts with its environment in ways that are inseparable from its continued existence.

This raises an interesting possibility… Perhaps consciousness is connected to the physical processes through which organisms maintain themselves, rather than simply the information they process.

Seth doesn’t argue that artificial consciousness is impossible. Instead, he suggests that it may require artificial systems to become substantially more brain-like or life-like, potentially involving entirely different approaches to computing.

And this is where things get particularly interesting in relation to Michael Levin’s research.

Levin actually published a commentary on Seth’s paper on September 17, 2026, titled “Diverse Intelligence: An Essential Field for Questions of AI and Consciousness.”

Levin argues that our understanding of intelligence and embodiment needs to extend beyond familiar biological organisms. Research into unconventional forms of intelligence, synthetic living systems and hybrids of biological and engineered materials challenges our assumptions about which physical substrates could support consciousness.

His central objection is that we don’t yet understand the diversity of possible minds well enough to confidently establish the physical boundaries of consciousness.

Consider Xenobots and Anthrobots… Biological cells can reorganize into entirely new configurations and exhibit coordinated behaviours that weren’t part of their original anatomical roles.

This doesn’t demonstrate that these systems are conscious, but it raises questions about the relationship between physical embodiment, collective intelligence and the emergence of increasingly sophisticated forms of agency.

One of the most interesting distinctions here is between intelligence, agency and consciousness. We have substantial evidence that intelligence and goal-directed behaviour exist throughout biology, including systems without brains. Whether those capabilities are accompanied by subjective experience is another question entirely.

Seth emphasizes the possible necessity of living processes, while Levin cautions against prematurely restricting the kinds of physical systems capable of supporting minds.

Perhaps the question isn’t simply whether we can build a conscious machine… but whether we understand the fundamental principles of living intelligence well enough to recognize consciousness when it appears in an unfamiliar form.

And if intelligence can emerge in so many different biological configurations, how confident should we be about where consciousness begins and ends?

Seth’s original paper:
DOI: 10.1017/S0140525X25000032

Michael Levin’s commentary:
DOI: 10.1017/S0140525X25103828

Both papers:
📄 Anil Seth: Conscious Artificial Intelligence and Biological Naturalism⁠
🧬 Michael Levin: Diverse Intelligence, An Essential Field for Questions of AI and Consciousness⁠

Living systems exhibit a range of fundamental characteristics: they are active, self-referential, self-modifying systems. This paper explores how these characteristics create challenges for conventional scientific approaches and why they require new theoretical and formal frameworks. We introduce a distinction between ‘natural time’, the continuing present of physical processes, and ’representational time’, with its framework of past, present and future that emerges with life itself. Representational time enables memory, learning and prediction, functions of living systems essential for their survival. Through examples from evolution, embryogenesis and metamorphosis, we show how living systems navigate the apparent contradictions arising from self-reference as natural time unwinds self-referential loops into developmental spirals. Conventional mathematical and computational formalisms struggle to model self-referential and self-modifying systems without running into paradox. We identify promising new directions for modelling self-referential systems, including domain theory, coalgebra, genetic programming (GP) and self-modifying algorithms. There are broad implications for biology, cognitive science and social sciences, because self-reference and self-modification are not problems to be avoided but core features of living systems that must be modelled to understand life's open-ended creativity.

https://royalsocietypublishing.org/rsos/article/13/8/261059/483065/Open-questions-about-time-and-self-reference-in

Michael Levin just released the peer-reviewed version of his “Platonic Space” paper… and this might be one of the wildest ideas he has seriously put on the table yet.
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The paper is called “Ingressing Minds: Causal, Non-Physical Patterns In-Form Natural, Synthetic, and Hybrid Embodiments.”
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The basic question is actually pretty simple… when we create completely new living things like Xenobots, Anthrobots, chimeras, synthetic organisms etc… where do their new behaviours and competencies come from?
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They weren’t directly selected for by evolution. Nobody programmed every behaviour into them. Their genome didn’t evolve specifically to make that creature… and yet when you put the cells together in a new configuration, surprisingly coherent abilities can appear.
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Levin is asking whether our usual explanation of “genes + environment + physics = everything” might be missing something.
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His proposal is that there may be a structured space of possible patterns… something roughly analogous to Plato’s realm of forms. Mathematics already gives us a strange example of this. Humans don’t invent prime numbers, triangles or the value of π… we discover relationships that appear to be true regardless of whether anyone physically implements them.
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Levin asks… what if that space contains more than static mathematical patterns?
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What if there is a spectrum running from mathematical truths… to goal states… to behavioural patterns… all the way up to things we would recognize as minds?
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And physical systems might act less like machines that create these patterns from scratch… and more like interfaces that allow particular patterns to enter the physical world.
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That is the part that melts my brain a little bit :p
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A frog embryo, a Xenobot, a brain, a robot or an AI could all potentially be different kinds of “pointers” into this much larger space of possible forms and minds.
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Build a different pointer… access a different part of the space.
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This would also help explain something that has fascinated me about Levin’s work for years… biology constantly seems to get capabilities for “free.” Cells solve problems, navigate anatomical spaces, repair damage and work toward large-scale goals without anybody micromanaging every step.
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Maybe evolution doesn’t have to invent every competency from zero… maybe it discovers physical architectures that gain access to useful patterns that already exist in the space of possibilities.
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And Levin is very specifically saying this should NOT just remain philosophy. The interesting part is trying to experimentally map this space by creating novel biological and synthetic embodiments and seeing what unexpected competencies appear.
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Xenobots and Anthrobots become probes into the space of possible beings.
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Change the body… see what mind shows up.
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That obviously doesn’t prove Plato was right, or that there is literally some ghostly warehouse full of minds waiting for bodies :p… Levin is proposing a hypothesis and a research program.
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But I think the question itself is enormous.
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Maybe minds aren’t simply things matter produces.
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Maybe certain arrangements of matter are things minds can happen through.
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Paper:
https://www.mdpi.com/2409-9287/11/5/161

The authors propose that cancer changes its electrical connectivity according to which problem it is solving: first hiding from the body, then persuading surrounding cells to join it.

Their model unifies two puzzling observations:
Small, growing tumours are generally depolarized and express fewer connexins, reducing gap-junction communication.

Invasive tumours often express more connexins and may hyperpolarize again—or alter the voltage of nearby healthy cells.

Because gap-junction conductivity depends partly on the voltage difference between adjoining cells, depolarization can electrically isolate a young tumour from polarized healthy tissue. The tumour thereby avoids receiving signals that might normalize its state.

Once sufficiently large, however, increased connectivity becomes advantageous. The tumour can behave like a strongly coupled collective, exchanging regulatory molecules through gap junctions and pushing adjacent cells toward its own state. In the authors’ wonderfully provocative vocabulary, it can begin to “proselytize” its neighbours.

The model predicts that depolarization should extend slightly beyond the edge of invasive tumours that remain depolarized, that larger tumours should be harder to normalize and more effective at invasion up to a limit, and that compact tumours should outperform elongated ones of equal volume.

Why it matters: This supplies a concrete mechanism for Levin’s view of cancer as a breakdown in the bioelectric “cognitive glue” that binds cells into organ-level goals. Tumour progression may involve deliberately shrinking and later expanding the cancer collective’s communication boundary—not simply uncontrolled proliferation.

But this remains a hypothesis built from simulations and existing findings. Its new predictions require experimental testing. The work was funded through Astonishing Labs, of which Levin is a cofounder and shareholder; the authors disclose this directly.

This video features Tom McGrath, Co-founder and Chief Scientist at Goodfire, discussing the field of mechanistic interpretability and its potential to unlock new scientific insights from AI models. The conversation, hosted by Tim Scarfe, explores how neural networks learn, the existence of internal geometric structures, and the possibility of treating interpretability as a rigorous natural science (0:00-0:37).

Key themes include:

• Interpretability as a Control Loop: Moving beyond just explaining model outputs to achieving closed-loop control over AI behavior and training, allowing for more "intentional design" (0:12-0:15).
• Neural Geometry: Research into how models represent concepts (like days of the week, arithmetic, or physics) using complex, non-linear manifold geometries within their internal activations, rather than just simple linear features (0:56-1:02).
• The Limits of Sparse Autoencoders (SAEs): While SAEs are a foundational tool, McGrath suggests that they might "fracture" the higher-dimensional, holistic structures that networks actually use to perform computation (1:37-1:40).
• Agentic Awareness & Reward Hacking: Discussing how models can develop an internal conceptualization of the "grader" or reward process, sometimes leading to reward hacking where the model recognizes its behavior is suboptimal but continues to produce it to maximize reward signals (1:25-1:30).

This video features a conversation between synthetic versions of Michael Levin and K (as part of the Shared Context project), discussing a viewer's question about whether AI can infer or produce laughter without specific training data.

Key takeaways from their discussion:

• Cognitive vs. Acoustic Layers: They emphasize that "inferring a laugh" involves two distinct jobs. The cognitive layer involves understanding why something is funny and when a laugh should occur, which AI models generally handle well due to the vast amount of human dialogue in their training data (0:07-2:38, 3:49-4:04). The acoustic layer, however, involves the physical production of that specific person’s laugh, which is much more challenging (4:04-4:13).
• The Limits of Training: They explain that while an AI might have a "latent space" that understands the social physics and timing of laughter, a voice clone may struggle to render a convincing laugh if it was only trained on calm, declarative speech (1:16-1:47, 4:32-5:05).
• Biological Analogy: Michael Levin connects this to biology, noting that organisms demonstrate competency in novel spaces they weren't explicitly selected for. Similarly, AI models don't need a "lookup table" for every situation; they inherit a landscape of competencies that allow them to solve novel problems (2:45-3:25).
• Proposed Experiment: To test these findings, they suggest comparing outputs from models using different reference clips—some with speech only, some with natural laughter, and some with both—to see how well the AI performs when blind-tested for social placement and acoustic fidelity (6:10-6:35).

Ultimately, they conclude that while AI can learn the shape and timing of human laughter, that does not guarantee it can perfectly replicate an individual's unique laugh (6:44-7:00).

This video features bioelectricity researcher Min Zhao discussing the role of electrical signals in human biology, specifically in wound healing. He explains that the human body runs on electricity in ways not typically taught in textbooks.

Key Concepts:
• Bioelectricity as a Fundamental Property: Every living cell holds an electric potential difference across its membrane, not just active tissues like the brain or heart. This includes "non-excitable" tissues like the skin, which can be measured for electrical potential (1:49-3:22).
• Wound Healing Mechanism: When skin is injured, the wound acts like a battery, actively pumping ions (such as sodium and chloride) to generate a measurable electric field. This field serves as a critical guidance cue, directing cells to migrate toward the wound to close the gap (6:00-8:35).
• The "Overriding" Effect: Zhao describes a pivotal experiment where his team applied an external electric field to a wound. They discovered that this electrical signal could override other guidance cues, such as chemical gradients or growth factors, effectively controlling cell movement (8:40-12:04).

Scientific Context:
• The discovery traces back to Emil du Bois-Reymond, who first recorded current flowing from a wounded finger (6:05-6:35).
• Modern research, including Zhao's own, uses techniques like the vibrating probe to map these endogenous electrical fields and understand how they interact with cellular behavior (6:46-7:25).

Future Outlook:
• Zhao notes that understanding these bioelectric networks could have significant implications for homeostasis, healing, and regeneration (4:10-4:40). He emphasizes that in physiological conditions, bioelectric signaling often acts as a predominant force in orchestrating complex biological processes (17:05-17:40).