MIT Scientist on Unifying Cognition and Biology | Manolis Kellis
What this covers
In today’s episode, MIT computational biologist Manolis Kellis dive into the hidden patterns linking DNA, evolution, and cognition, exploring a potential unifying theory that bridges biology, AI, and the essence of life.
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LINKS MENTIONED: - Manolis Kellis’s Lab (website): https://compbio.mit.edu/ - Manolis Kellis’s profile: https://web.mit.edu/manoli/ - Curt’s article on language: https://curtjaimungal.substack.com/p/language-isnt-just-low-resolution - Chiara Marletto on TOE: https://www.youtube.com/watch?v=Uey_mUy1vN0 - Roger Penrose on TOE: https://www.youtube.com/watch?v=sGm505TFMbU
TIMESTAMPS: 00:00 - Introduction 02:05 - The Scope of Biological Unification 06:02 - Biology vs. Physics 09:31 - DNA as Life’s Language 13:45 - The Universal Compatibility of DNA 16:55 - Evolutionary Trade-Offs and Isolation 20:17 - Layers of Abstraction in Biology 24:51 - Beyond DNA: The Role of Histones 30:30 - Protein Folding and Function 35:26 - How Cells Interpret DNA Signals 40:24 - The Creativity of Language and Miscommunication 44:55 - Teaching and Simplification 51:09 - Evolution of Cognition and Centralized Decision-Making 57:35 - Vertical vs. Horizontal Evolution 1:04:20 - Specialization and Society’s Role in Evolution 1:08:50 - The Future of Biological Understanding
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Kellis argues that biology, cognition, and artificial intelligence are unified by a single principle: iterative function-fitting through tunable parameters organized in abstraction layers, rather than fixed laws like physics, enabling unprecedented convergence for understanding disease and transforming medicine.
- Biology operates through endless tinkering with tunable parameters across thousands of interacting components, unlike physics which follows unchanging laws
- All adaptive systems—from genomes to brains to AI—build complexity through hierarchical abstraction layers that allow reuse and recombination of building blocks
- Modern AI and convergent technologies now allow simultaneous analysis of genes, proteins, pathways, and patient data to reveal modular disease mechanisms, making personalized medicine feasible
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Some species like Candida have undergone recoding events where they mutated their tRNA to recognize different codons, effectively changing their genetic code, likely as a defense mechanism against viruses that could not longer infect them because viral code would be incompatible with the recoded machinery.
“In my own work, I ran into a species of candida that basically recoded its DNA, potentially to fight against viruses that would attack it... because I was about to say, it sounds like it's disadvantageous to not have the same, to not speak the same language. Correct. It's disadvantageous if you want to exploit your environment. But if you're getting attacked by your environment, then by recoding your genetic code, you basically have the advantage of now viruses can just simply throw in a piece of software that your cells will run.”
Candida species recoded its DNA, potentially to fight against viruses that would attack it, gaining the advantage that viruses can no longer simply insert software that cells will run because that software no longer makes sense in the recoded cell.
“In my own work, I ran into a species of candida that basically recoded its DNA, potentially to fight against viruses that would attack it... by recoding your genetic code, you basically have the advantage of now viruses can just simply throw in a piece of software that your cells will run. Because that piece of software will not make sense in the milieu of your cell.”
Physics has unchanging laws written 13.8 billion years ago that apply uniformly everywhere, whereas biology is fundamentally defined by creating niches and constantly rewriting its rules to fit those niches, so biology has a gazillion exceptions that are still being written rather than a small number of universal rules.
“So physics started 13.7 billion years ago, let's say. So physics as we know it... beyond that, every neighborhood speaks the same language. So Andromeda and Earth have the same physics. Biology is very different. Biology in, I don't know, the thermal vents of the bottom of the ocean is very different than the biology in the Sahara Desert”
Nucleotides generally do not care about quantum effects; the beauty of abstraction layers is that you can abstract away variability at the lowest level, encapsulated in the reading machinery that interprets ACGT without needing quantum-mechanical details.
“Does the nucleotide care about quantum effects? Most of the time, no. Most of the time, no. Most of the time, no. That's the beauty of these layers of abstraction. You can abstract away. Abstraction means you're separating it. You're not looking at the details anymore.”
We can now understand gene function through multiple modalities: literature via large language models, knowledge graphs of disease-gene-drug interactions, protein structure predictions, gene expression patterns across thousands of datasets, and protein-protein interaction networks, allowing convergent multi-faceted representations of single genes.
“you can look in the literature... large language models to capture every single paper that has been written about that gene... represent the function of a gene from the entire medical literature. We can represent the function of the same gene using knowledge graphs... the anatomical regions where this gene is expressed... think about that gene as a collection of amino acids and the structure that this gene has... look at that same gene and look at its expression patterns... protein-protein interaction network”
The genetic code shows evidence of design by compatibility constraint rather than physical necessity: bacteria that received foreign DNA without the same genetic code would go extinct because they couldn't leverage evolved proteins from other species, so the genetic code was standardized across all life through software-like compatibility requirements.
“When viruses were exchanging DNA and when bacteria were exchanging DNA with each other, a bacterium that didn't have the same genetic code would go extinct because it wouldn't be able to leverage the evolved proteins of other species that get passed on to it. So the genetic code started out with variations, but then those variations were eliminated because of software compatibility”
Writing for a popular audience forces deeper understanding of one's own technical ideas, not by simplification but by reformulation from a different perspective, which was demonstrated by Chiara Marletto and Roger Penrose in their popular books.
“people that I've interviewed like Chiara Marletto of Constructor Theory and Roger Penrose...even though they're writing to a popular audience, they say that they understand even their more technical ideas more precisely because they've had to reformulate them from another perspective. It's not always that I have to simplify it to the common denominator. It's just the act of formulating it.”
The genetic code became universally standardized across all life because bacteria that used a different genetic code would go extinct, unable to leverage proteins evolved by other species through horizontal gene transfer—demonstrating software compatibility selection.
“When viruses were exchanging DNA and when bacteria were exchanging DNA with each other, a bacterium that didn't have the same genetic code would go extinct because it wouldn't be able to leverage the evolved proteins of other species that get passed on to it. So the genetic code started out with variations, but then those variations were eliminated because of software compatibility, because of that communication.”
We can now understand a gene's function through multiple facets: literature via large language models, knowledge graphs showing disease interactions and chemicals, protein structure via protein language models, expression patterns across datasets, and protein-protein interaction networks.
“What does a gene do? Well, you can look in the literature... we can use large language models to capture every single paper... We can represent the function of a gene from the entire medical literature. We can represent the function of the same gene using knowledge graphs. What are the diseases that this gene interacts with... We can look at that same gene and look at its expression patterns across thousands of datasets.”
We are now at an extraordinary convergence that allows us to peek into the building blocks of biology, disease, and human heterogeneity at a scale that was unfathomable 20 years ago.
“We now are sitting in this extraordinary convergence that allows us to peek into the building blocks of biology, into the building blocks of disease, into the heterogeneity of different individuals, different patients, different tissues, different organs, different cell types, at a scale that was unfathomable 20 years ago.”
The central dogma of biology—DNA makes RNA makes protein—provides the fundamental unification principle that allows understanding of all life despite organismal diversity, because all organisms from bacteria to plants to animals share the same underlying information processing system.
“the concept that even though some organisms are animals, others are plants, others are viruses and bacteria, there is still some fundamental principle that biology can reveal. And that started with the elucidation of DNA as the basis of life and inheritance, and how it makes RNA and how it makes protein”
It took a billion years for multicellular life to appear, but after multicellularity emerged, extraordinarily complex body plans appeared very rapidly, and the human neocortex expansion happened faster than almost anything in evolutionary history, suggesting that better modularity and hierarchical organization enable accelerating complexity.
“if you look at the history of life on this planet, it took a billion years for multicellular life to even appear... after you have multicellularity, you start seeing extraordinarily complex body plans very, very rapidly. And after you have those, you start seeing increasing cognition very, very rapidly... the human brain evolved in a blink of an eye in evolutionary terms. This expansion of the neocortex is something that happened faster than almost anything in evolution.”
Neurons develop through simple developmental programs that create layers of neurons which specialize through communication with each other, and synapses are reinforced or weakened based on feedback signals they receive, so the developing brain is an adaptable neural network that tunes itself to match the complexity of the environment it encounters.
“If you look at our neurons, we basically have a very simple developmental program that gives rise to our entire brain... the neurons are basically specializing. They're communicating with each other... synapses that are reinforced or weakened based on the signals that they receive and the feedbacks that they receive. So, it is an adaptable neural network based on the information that it is processing.”
Gene domains and protein folds are reused and recombined across evolution; a fold discovered in one protein context gets duplicated, tinkered with, and recombined with other folds to create new primitives, exemplifying the principle of building blocks upon building blocks.
“a protein domain that you have created. You have a fold. That fold will get reused and reused and reused and then recombined with other folds to sort of now create a new primitive. And that new primitive will now get, you know, again, replicated in different places and reused. You not only have vertical evolution of a gene changing, but you also have duplications of these genes where you now have two copies that you can continue tinkering with.”
DNA has four bases and amino acids have 20 versions, but expressivity and power comes from combinatorics—single components have limited meaning in isolation but gain emergent properties through combination.
“The building blocks are small and numbered. So DNA has four bases. Amino acids have 20 versions. But the power in expressivity comes from the combinatorics.”
The syntax of genes includes that genes start with ATG and end with stop codons, and splicing rules that govern how segments are joined; semantics emerges at every level from regulatory control to protein folding to organism phenotype.
“you can think of syntax as simply the fact that every gene starts with ATG, and it ends with one of the three stop codons... you can also think of syntax as splicing... Now, semantics, gosh, where do I start? It is so, so beautiful. There's layers and layers and layers, again, of semantics”
Evolution works by tinkering with subtle functions and thousands of tunable parameters across gene regulatory networks; organisms with better evolvability—the ability to evolve and adapt—succeed because they can modify their regulatory circuits to fit new environmental niches, making evolvability itself an evolved trait that becomes more sophisticated over time.
“Evolution doesn't work by giant, you know, knobs that you're turning one way or another way. Evolution works by being able to tinker with subtle functions. And this tinkering is what I call evolvability. In other words, you get good at evolving, you get good at adapting.”
It seemed mad for one research group to work on genetics, gene regulatory elements, epigenomics, protein structure, knowledge graphs, patient data, and electronic health records separately, but now there is method to the madness because you must integrate all information to understand complex diseases like Alzheimer's, obesity, schizophrenia, cardiovascular disease, immune disorders, cancer.
“It seemed mad for any one research group to be working on all these different areas. But now there's method to the madness. It's all coming together. And it's coming together in the most beautiful integrative way. Because in order to understand the complexity of something like Alzheimer's or obesity or schizophrenia or cardiovascular disease or immune disorders or cancer or immunotherapy response. You can't separate all of that information.”
Humans have conquered natural selection by overcoming burdens of reproduction and shelter, so humans may have largely stopped genetic evolution and instead entered a new trajectory of cultural evolution—what Kellis calls horizontal evolution rather than vertical evolution—where knowledge and ideas spread horizontally through societies rather than vertically through family lineages.
“we have basically conquered natural selection. We have overcome the burden of reproduction and shelter... humans is no longer about, you know, being the strongest or being the smartest or being the, you know, fittest... humanity has taken a new trajectory. And that trajectory is not one of genetic evolution, but perhaps one of cultural evolution. This is what I call horizontal evolution, instead of vertical evolution.”
DNA methylation (adding CH3 groups to cytosine bases) alters gene regulation without changing the base pairing: methyl-C still pairs with G through three hydrogen bonds but bulges slightly, allowing some regulatory proteins to recognize methyl-C specifically while others recognize either form, creating a repressive epigenetic mark.
“a C, most of the time, is a C. But it can also undergo methylation. For example, you can add a CH3 group to your C, and now it's a methyl C. Now, the methyl C, most of the time, will be read as a C. It still binds a G. It still has the three hydrogen bonds. But on the side, it, you know, bulges a little... genomes have figured out a way to use methyl C as a repressive mark”
Only 1.5% of the human genome codes for proteins while 98.5% does not; within the 98.5% are non-coding regions, spacer sequences, repetitive elements, and crucially regulatory control regions that govern where proteins bind, where silencers operate, how DNA is organized, where nucleosomes are placed, and all other aspects of gene control.
“1.5% of the human genome codes for protein. 1.5%, tiny fraction. 98.5% does not code proteins. Somewhere in that 98.5 is a bunch of garbage, a bunch of spacer, a bunch of repeat elements... and a bunch of control regions that govern where the proteins will bind to turn on other genes, where the silencers will bind”
The genome generates meaning through DNA regulatory proteins (transcription factors) binding to specific DNA sequences; the proteins recognize the 3D shapes of stacked base pairs from the outside of the double helix (the 'wood' in an iron-wood ladder analogy), not the phosphate backbone, so the pattern of which regulators bind to a region determines its regulatory meaning.
“when a protein binds this ladder, it binds from the outside. It binds the atoms that are facing outside... the backbone of the DNA is just a phosphate backbone... the affinity that gives rise to the recognition of a sequence pattern comes from feeling the sides of the bases... there's like, let's say there's an iron ladder, and the bases themselves are made of wood. It's the wood that gets recognized.”
Chemotaxis is the ability of cells to sense and align themselves to chemical gradients, representing the simplest form of sensing and the beginning of cognition, where organisms move toward higher concentrations of nutrients like sugar.
“to become fitter than their competitors, they need to start chasing chemicals, figuring out where is the highest concentration of sugar, also known as chemotaxis. They're able to align themselves to chemical gradients. That's the beginning of sensing.”
We now have the ability to peek into the building blocks of biology, disease, and individual heterogeneity at cellular and tissue scales across different patients and organs in a way that was literally unfathomable 20 years ago.
“We now are sitting in this extraordinary convergence that allows us to peek into the building blocks of biology, into the building blocks of disease, into the heterogeneity of different individuals, different patients, different tissues, different organs, different cell types, at a scale that was unfathomable 20 years ago.”
Teaching forces deeper understanding: by formulating ideas for others, articulating definitions, and answering questions that reveal gaps, teachers must re-understand foundations, update knowledge in their field, and fill explanation gaps, which advances their own research.
“I'm constantly forced to go back to basic principles... if I'm an outsider, how will I understand this field?... every single year to sort of re-give the same lectures, but now with all of the new advances... And I think that that advances my own research.”
When students ask questions showing apparent misunderstanding, those are the most powerful questions because if one student is confused that way, likely many others are too, and the confusion reveals an ambiguity in the teaching that the instructor must fix.
“sometimes those questions show a complete lack of understanding. Those are the most powerful questions. Because if they're misunderstanding, chances are there's like 20 other people who are misunderstanding... no, no, the way that I interpret this question is entirely my fault.”
The acceleration of civilization is driven by the unification of the world through trade routes and information exchange, enabling ideas to spread and be adopted globally much more rapidly than when populations were isolated.
“part of what has led to the acceleration of civilization is the fact that we have unified the world. The trade routes of being able to sort of take silk and, you know, trade it with glass, or you name it, basically means that you are now exchanging information much, much more. And therefore, a new idea will be adopted and spread much more rapidly.”
Humanity has taken a new trajectory: cultural evolution replacing genetic evolution, where vertical lineages of knowledge were replaced by horizontal spread of ideas across communities.
“Right now, humanity has taken a new trajectory. And that trajectory is not one of genetic evolution, but perhaps one of cultural evolution. This is what I call horizontal evolution, instead of vertical evolution. So vertical evolution is passing on your genes. And with humans, vertical evolution also included passing on your knowledge to your village.”
Scientists should not specialize so narrowly that they avoid exposure to other fields—cross-disciplinary exposure generates novel insights; however, core depth should remain specialized rather than becoming broad generalists.
“Now in science, does that mean that you should stop looking at stuff outside your field? No, no, not at all. Because being better at understanding physics might give you new ideas for biology, and vice versa, or AI to physics.”
Modern AI is fundamentally different from classical AI—classical AI used rule-based expert systems with predetermined flowcharts, while modern AI uses neural networks and representation learning to fit functions through tuning thousands of parameters.
“Classical AI initially was about rule-based systems... classical AI was all about designing systems that appear intelligent. It was all about sort of building expert systems... the flowchart was already determined by a human expert. Like if-then statements... The advance of neural networks is that you could suddenly start modeling the world using this type of tinkering.”
Physics has unchanging laws written 13.8 billion years ago, while biology operates through endless tinkering and constantly rewrites its own rules, creating a plethora of exceptions with no fixed laws.
“Unlike physics, which follows unchanging laws, biology seems to operate through endless tinkering, constantly rewriting its own rules.”
The next generation of science comes from cross-pollination of specialties rather than requiring pure interdisciplinary scientists; Kellis values having pure physicists, mathematicians, biologists, chemists, and experimentalists working together because diversity in thinking approaches—not just topic blending—drives progress.
“I don't want just interdisciplinary scientists all coming to my lab. No, I want the pure physicist, and I want the pure mathematician, and I want the pure biologist, and I want the pure chemist, and I want the pure experimentalist, all working together under one roof. Because diversity is the power of humanity.”
Personalized medicine is economically feasible only if treatments are modular: instead of one pill per patient (impossibly expensive), we need pills for each dysregulated pathway, where each pathway is shared by millions in different combinations, making economics viable through modular combinatorics.
“for personalized medicine to function both in terms of feasibility and in terms of economics. You cannot simply say I want a pill for a person... the economics work out. Because that pathway is shared by millions of people in different combinations... we can now start thinking about personalized medicine in a way where I can take your own genome... create for every patient their own pill. Which will be a combination of all of these different tinkerings”
Modern societies can specialize: we don't need everyone to learn math or do sports; we can allow humans to excel in thousands of different directions because society takes care of those who lack certain abilities, enabling specialization that makes civilization work.
“we as a society are there to help. And the beauty of that is that you can now allow humans to excel in thousands of different directions... I think it's okay to do specialization. That's what made our society work. Like the concept that every one of us has to be good at everything is ridiculous.”
Cognition emerges when organisms integrate multiple conflicting sensory streams to build a model of the world and act on a single decision based on that model, rather than responding independently to each sensory input; this integration marks the transition from decentralized to centralized control.
“So that's the point in evolution where you have cognition arising. The concept of a central nervous system. The concept of I can integrate that information to build a model of the world and then act upon a single decision for that model of the world.”
The highest impact of AI will be understanding life and medicine; solving biology and medicine is a more complex challenge than solving language because biology has billions of tunable parameters producing endless adaptive functions across niches, whereas language is constrained by evolved human cognition.
“The highest impact of AI, and perhaps the highest challenge of it all, will be understanding life and medicine, sort of improving the human condition... solving language is a much simpler problem than solving biology or solving medicine... understanding this tinkering, this extraordinary diversity in functions and adaptations in niches that biology has”
Disease mechanisms can be understood modularly: Alzheimer's is not a single monolithic disorder but a collection of interconnected pathways (cholesterol transport, lipid dysregulation, amyloid accumulation, tau pathology, microglial clearance, neuroinflammation, neurovascular dysfunction) that are reused in different combinations across different diseases.
“Instead of just saying it's a monolithic disorder and everybody has Alzheimer's. We can say well wait a minute. There's building blocks. There's cholesterol transport. There's lipid dysregulation. There's amyloid accumulation. There's tau pathology. There's microglial clearance. There's neuroinflammation. There's neurovasculature unit dysregulation.”
Writing books for popular audiences, though challenging, forces researchers to understand their own technical ideas more precisely—reformulation for non-specialist audiences reveals gaps and deepens technical understanding.
“Why don't you write another textbook? Why are you writing for a popular audience? And people that I've interviewed like Chiara Marletto of Constructor Theory and Roger Penrose and so on, even though they're writing to a popular audience, they say that they understand even their more technical ideas more precisely because they've had to reformulate them from another perspective.”
The same pathways underlying Alzheimer's are reused in different ways in cardiovascular disease, frontotemporal dementia, and schizophrenia—suggesting that diseases share modular building blocks that can be tackled individually.
“The same pathways that appear to be underlying Alzheimer's are reused in different ways in cardiovascular disease. And they're reused in different ways in frontotemporal dementia and in schizophrenia. So we can now start understanding what are the points of convergence. What are these primitives? What are these building blocks of disease.”
It took a billion years for multicellular life to appear, then extraordinary complexity emerged very rapidly, faster than almost anything in evolution—this acceleration reflects increasing evolvability through better modularity, hierarchy, and abstraction.
“It took a billion years for multicellular life to even appear. And then you would expect that with genetic algorithms, increasing complexity will require more time. But in fact, we see exactly the opposite. After you have multicellularity, you start seeing extraordinarily complex body plans very, very rapidly.”
The building blocks of physics are elementary particles from the standard model; the building blocks of biology are ACGT and 20 amino acids; these building blocks are organized through different rules and patterns of connectivity that give rise to higher levels of abstraction, mirroring physics and biology's parallel layering.
“the building blocks of physics are basically the elementary particles of the standard model. And in the same way, the building blocks of biology are ACGT and the 20 amino acids... the connectivity pattern between them that give rise to the higher levels of abstraction.”
Google DeepDream visualizations where AI interprets image pixels using learned primitives from another domain (like showing giraffes made of eyes) demonstrate how AI represents images as combinations of learned building blocks; the AI reuses its learned primitives to construct novel images.
“recall 10 years ago or so, when Google Dream or so came out, something like that. And there was these trippy psychedelic images, where a dog became eyes, and they were all eyes for 10 seconds... you can basically ask, can I represent the pixels associated with this image using primitives from another domain? For example, you want to represent giraffe pictures, but your primitives are all about eyes. So you're not going to have giraffes built of eyes.”
Large language models can represent every word as a vector in space, and that vector's meaning shifts based on surrounding context; a word like 'apple' means differently in 'fruit basket' context versus 'Apple Computer' context, mirroring how pixels and atoms gain meaning from their neighbors.
“I can basically start with these primitives of language. I can start with large language models that represent every word as a vector somewhere in space. And this vector takes a new meaning from the words surrounding it. So the word apple in the context of a fruit basket is very different than the word apple in the context of a PC or a Macintosh.”
Large language models represent each word as a vector in space, with meaning coming from contextual embedding in relation to surrounding words, enabling generalization from training data to novel contexts without explicit rule encoding.
“large language models that represent every word as a vector somewhere in space. And this vector takes a new meaning from the words surrounding it. So the word apple in the context of a fruit basket is very different than the word apple in the context of a PC or a Macintosh.”
Students asking questions that reveal complete misunderstanding are the most powerful questions because if one student misunderstands, likely 20 others do too—the fault is with the teacher's explanation, not the student's comprehension.
“When students ask a question in the class, sometimes those questions show a complete lack of understanding. Those are the most powerful questions. Because if they're misunderstanding, chances are there's like 20 other people who are misunderstanding.”
The distinction between tinkering with pixels versus tinkering with concepts is crucial—exponential growth happens when you stop tinkering with pixels and start tinkering with concepts, which is the unifying emergent property across evolved systems fundamentally different from physics.
“The way that you get to this exponentially faster growth is when you stop tinkering with pixels, but you start tinkering with concepts. And that's, in my view, this unifying emergent property of these evolved systems that is fundamentally different from how physics works, but appears to be somehow unified across AI, across cognition, across evolution, across genomes, across gene regulation, across every aspect of living, adaptable things.”
The DNA in cells is not naked but packaged into nucleosomes made of 8 histone proteins (2 copies each of H2A, H2B, H3, H4) with ~150 nucleotides wrapped around each nucleosome, and these histone proteins undergo chemical modifications like acetylation, methylation, and ubiquitination that affect DNA compactness and accessibility.
“the DNA is not swimming around naked inside our cells. It is packaged in nucleosomes. Nucleosomes are made out of eight histone proteins. Most of the time, H2A, two copies, H2B, two copies, H3, two copies, H4, two copies. And there's about a hundred and fifty nucleotides that are wrapped around every single one of these nucleosomes.”
tRNA molecules were hypothesized by Francis Crick as the adapters between DNA and protein before they were discovered, because RNA is the only molecule with dual capability: it can bind DNA through complementarity and also bind proteins through 3D structure, making it uniquely suited for the translation function.
“that translation table requires having a set of tRNAs. These are adapter molecules... Francis Crick actually said, oh, whatever molecule is the adapter between DNA and protein must be an RNA. Because an RNA has a dual capability of a binding the DNA with complementarity and binding the RNA. And on the other side, it has the ability of binding a protein based on 3D structure.”
Classical AI was rule-based and designed by human experts who predetermined all logic paths; modern neural networks enable function-fitting by learning through multiple parameter layers that approximate nested functions, marking a shift from explicit rule design to learned representations.
“classical AI initially was about rule-based systems... the expert would come in with all of the knowledge, all of the rules, all of the decision, and we would simply build the sensory part where the AI will observe what's happening in the world and then make decisions that are predetermined... The advance of neural networks is that you could suddenly start modeling the world using this type of tinkering... thousands of parameters being updated through multiple layers”
ChatGPT as a learning tool enables engagement with complex material by breaking down papers and concepts on demand, allowing students to explore rabbit holes of learning without gatekeepers, fundamentally changing accessibility to knowledge.
“by having ChatGPT as your partner in all of your learning, by tackling the most complex paper and then saying, hey, break it down for me. And sort of, oh, tell me about that concept. And now you can sort of build it back up and sort of dive as far as the rabbit hole will go.”
Learning mathematics is made more accessible when presented with extraordinary visualizations and beauty (like 3Blue1Brown channel) that reveal why mathematics matters before students engage with rote procedures, enabling excitement to drive engagement with foundational material.
“Dive into YouTube channels that sort of expand mathematics with extraordinary visualizations. I'm thinking about three brown, one blue... My eight-year-old is loving it and truly understanding it and getting these concepts at a way that was not accessible before. And by seeing how beautiful the mathematics is later on, they're excited about the mathematics that they have to bear with today.”
Convolution is a revolutionary advance in AI because it learns shared convolutional filters that can be applied across an entire image, rather than having each neuron in isolation recognize different parts, enabling learning of reusable primitives like edge detectors.
“the concept of a convolution is this dramatic transition to a new type of AI. And that transition is about building representations...you're not letting every neuron in isolation recognize a different part of an image. But instead, you're learning these convolutional filters, these operations that you can then apply at every part of the image...You're sharing parameters across many parts of your image.”
The meaning of a protein arises from how a sequence of amino acids folds in three dimensions—the fold determines structure, and structure determines function through shape complementarity with other molecules.
“Where does the meaning arise? The meaning arises in the folds that the protein makes. So, basically, a set of amino acids, you know, a sequence of amino acids, will basically fold in three dimensions. That fold will determine its structure, and its structure will determine its function.”
Neurons develop through a simple developmental program that creates layers, with neurons specializing, communicating, and having synapses reinforced or weakened based on signals received—creating an adaptable neural network tuned by experience.
“We basically have a very simple developmental program that gives rise to our entire brain. Basically, it simply says, just create these layers of these neurons. As they build the layers, the neurons are basically specializing. They're communicating with each other... synapses that are reinforced or weakened based on the signals that they receive and the feedbacks that they receive. So, it is an adaptable neural network based on the information that it is processing.”
Humans are poor at doing many types of math and have no intuition about genomes, protein folding, or chemical interactions—therefore, AI brings a new class of computation and ways of understanding the natural world far beyond what human reasoning can achieve.
“Humans are pretty bad at doing all kinds of math. Humans have no intuition about genomes or protein folding or chemical interactions at all. So we now have the ability with AI to bring in a whole new class of computation, bring in all of these different ways of understanding the natural world that are far beyond language.”
Teaching mathematics by stripping away beauty creates disengagement, but exposing students to beautiful mathematics visualizations early through channels like 3Blue1Brown enables excitement about the foundations they must learn.
“A lot of our teaching of mathematics is unfortunately stripping away all of the beauty that you only get to later on. But dive into YouTube channels that sort of expand mathematics with extraordinary visualizations. I'm thinking about three brown, one blue... This does not exist when I was a student. And my eight-year-old is loving it and truly understanding it.”
The act of teaching forces creation of new abstractions and represents one of the most fundamental ways to deepen understanding beyond what's needed for mere working knowledge.
“Because every teacher is constantly looking for new abstractions, for new ways of conveying that knowledge to the next newcomer. And that is part of making that understanding so much deeper. So it's not that you don't understand it. The act of teaching, it forces you to understand it at a level beyond what you would need to just work with it.”
Learning to code is essential with no excuse—understanding building blocks of complex AI systems by reading tons of programs and tinkering with them is fundamental to future success.
“Advice number one is learn how to code. There's just no excuse not to. Learn how to program fundamentally by reading tons of programs, by understanding the building blocks of the most complex AI systems. Learn how to tinker with these complex systems.”
Humans are weird because we have conquered natural selection—overcoming reproductive burden and shelter requirements means humans no longer select for strength, smartness, or fitness in the traditional genetic sense.
“Humans is a little weird, because we have basically conquered natural selection. We have overcome the burden of reproduction and shelter. And in some ways, you could say that we stopped evolving, at least in the genetic sense, that basically humans is no longer about, you know, being the strongest or being the smartest or being the, you know, fittest, or you name it.”
Cognition emerges when organisms need to integrate multiple conflicting streams of sensory information to build a model of the world and make a single decision—before cognition, organisms could respond to each sense independently.
“So that's the point in evolution where you have cognition arising. The concept of a central nervous system. The concept of I can integrate that information to build a model of the world and then act upon a single decision for that model of the world.”
An extraordinarily intelligent being without language could not formulate the laws of physics—language provides stepping stones enabling understanding of progressively more complex concepts.
“I could be the most intelligent being on the planet... They have extraordinary cognition. Will they be able to formulate the laws of physics, starting from nothing without language? I think language gives us stepping stones that with which we're able to understand more and more and more complex concepts.”
Mitochondria were originally free-living protobacteria that were engulfed by a protoeukaroyte cell early in evolution, and this engulfment event froze the mitochondrial genome in time, which now has its own translation table and genetic code distinct from the host genome.
“if you look at mitochondria, mitochondria were engulfed very early in evolution. They were some protobacterium, if you wish. And it was engulfed by another protoeucariot that basically then became eukaryote...And that engulfment froze in time the mitochondrial genome. So, the mitochondrial genome now has its own translation table. Has its own code, if you wish.”
tRNAs are adapter molecules hypothesized by Francis Crick decades before discovery; they must be RNA because RNA uniquely has both the ability to bind DNA through complementarity and to bind proteins through 3D structure, making it the only molecule capable of translating between the DNA code and amino acid sequences.
“that translation table requires having a set of tRNAs. These are adapter molecules that basically take every triplet and then have bispecific translation. On one side, they're specific to the three letters. On the other side, they're specific to the amino acid that binds that RNA. So this was hypothesized decades before tRNAs were actually discovered, where Francis Crick actually said, oh, whatever molecule is the adapter between DNA and protein must be an RNA.”
Chemotaxis is the biological process by which organisms sense chemical gradients and move toward higher or lower concentrations; it's the simplest form of sensing and the beginning of how organisms detect their environment.
“they need to start chasing chemicals, figuring out where is the highest concentration of sugar, also known as chemotaxis...They're able to align themselves to chemical gradients. That's the beginning of sensing...Can you spell that? Chemo for chemical, taxes for aligning, like taxonomy. I see. So you align yourself to the chemical.”
DNA is packaged into nucleosomes containing eight histone proteins, and these histones can be modified through acetylation, methylation, and ubiquitination, which alter DNA compactness: more acetyl groups make DNA looser, removing them makes it tighter, creating physical properties that affect how genes are interpreted.
“The DNA is not swimming around naked inside our cells. It is packaged in nucleosomes. Nucleosomes are made out of eight histone proteins... there's about a hundred and fifty nucleotides that are wrapped around every single one of these nucleosomes. Now, that packaging itself can basically undergo acetylation or methylation or ubiquitination... the number of acetyl groups, for example, that you have can influence the compactness of DNA.”
1.5% of the human genome codes for proteins; 98.5% does not, and within that 98.5%, there is garbage (repeat elements), spacer DNA, and control regions (enhancers, silencers, nucleosome organizers) that govern where proteins bind to regulate genes, showing that protein-coding sequence is a tiny fraction while regulation is the dominant function.
“1.5% of the human genome codes for protein. 1.5%, tiny fraction. 98.5% does not code proteins. Somewhere in that 98.5 is a bunch of garbage, a bunch of spacer, a bunch of repeat elements that are just replicating for themselves, and a bunch of control regions that govern where the proteins will bind to turn on other genes, where the silencers will bind... the regulation, the control of DNA lies within that 98%, and the proteins lie in the 1.5%.”
The most beautiful relationship in the universe is that DNA evolved a construct (the brain and cognition) with its own language and code that can learn any language, and that construct then developed programming languages and created AI systems that develop their own learning and code about the natural world—a translation across all abstraction layers.
“the most beautiful relationship in the universe, perhaps, is the relationship between the fact that the DNA that has its own language and code evolved a construct that has its own language and code that is now a learning construct that can adapt to learn almost any language and code. And that in that language and code, we develop programming languages and human languages. And in those languages, we wrote constructs that now give rise to AI systems, which themselves can now develop their own learning and code and patterns about the natural world.”
Biology shows emerging principles of tunability, approximation, and fitting curves and functions to data—bacteria, humans, brains, societies, and computers all perform this same fundamental operation of tuning parameters to approximate functions.
“And those patterns are patterns of tunability, patterns of approximations, patterns of fitting curves and functions to data. And I would say that bacteria are doing this, humans are doing this, brains are doing this, societies are doing this, computers are doing this.”
ChatGPT can be used as a partner to explore large pieces of code that seem indomitable, breaking them down and asking it to explain concepts, enabling fearless diving into complex disciplines.
“Learn how to tinker with these complex systems. Use ChatGPT as your partner to explore giant pieces of code that would seem indomitable, but ask it to break them down for you. Basically, be fearless. Go out there and dive in to all of these different disciplines.”
Effort counts twice: effort combined with talent produces skill; skill combined with effort produces achievement; and achievement feeds back to increase enjoyment, creating a virtuous cycle that makes passive 'easy way' approaches ultimately limiting.
“Above the camera over there, it says, effort equals interest times enjoyment. Skill equals talent times effort. Achievement equals skill times effort. And effort counts twice. Namely, as you start putting in the work, you achieve better... effort along with talent gives you skill, but skill again along with effort gives you achievement. And achievement then feeds back into your enjoyment.”
There is no hope for traditional unification in biology because there are too many species and exceptions, unless one invokes such vague principles that they have little predictive power, but unification becomes possible through recognizing combinatorial complexity from limited building blocks.
“It seems like there's no hope then for unification in biology if there's such a plethora of species...how can you hope to unify them? Unless you're saying something so vague that it has little predictive power...The building blocks are small and numbered. So DNA has four bases. Amino acids have 20 versions. But the power in expressivity comes from the combinatorics.”
The human brain's non-neocortical components (limbic system, emotional systems, neurotransmitters, brain waves, hormonal influences) can be viewed either as bugs constraining optimal cognition or as features that push the brain into diverse local optima and enable solutions otherwise unreachable, suggesting AI might benefit from incorporating similar mechanisms.
“You can think of them as features or bugs. You could basically say, oh, we have this extraordinarily beautiful cognitive brain, but unfortunately it is influenced by all of these other things. Or you could think of them as features. You could basically say we have this brain that is constantly pushed into different local optima... because of all of these things. And therefore, it can arrive at solutions that it wouldn't normally arrive at”
Misunderstandings in conversation are creative because speakers construct mental models of others' ideas as they speak, and misheard utterances can lead thinking down novel paths neither participant anticipated; conversation is like tennis where sometimes the ball goes into unexpected woods.
“by misunderstanding each other, we are creating new meanings that might be much more interesting than the meanings that I had in my thoughts... So imagine two people are playing tennis, which is what we think a conversation is supposed to be. But sometimes you can hit the ball instead of toward the person over there into an interesting part of the woods.”
Language is not merely a low-resolution transmission channel for pre-formed thoughts but also a tool that creates meaning and scaffolds thinking; language provides primitives (basic concepts) that allow building layers of abstraction, and forgetting the primitives while building on them is how complex understanding emerges.
“I argue something that's akin to the opposite of that, that language is not only a process of transmission, but it's also of creation and excavation... Language gives you the primitives... I could be the most intelligent being on the planet... Will they be able to formulate the laws of physics, starting from nothing without language? I think language gives us stepping stones”
Humans should fearlessly learn foundational knowledge across disciplines using modern resources (YouTube, Wikipedia, ChatGPT), then tinker with complex systems rather than expecting to master basics before advanced work.
“be fearless. Go out there and dive in to all of these different disciplines... we have no excuse anymore to not understand something. We have at our disposal YouTube, and Wikipedia, and bioRxiv, and Archive, and ChatGPT... Use ChatGPT as your partner to explore giant pieces of code... I think that ability to start from complexity down rather than from simple up”
The genome has evolved a language with rules of communication, just as humans have language with words that mean something, allowing proteins to talk to each other and to DNA.
“In the same way that humans have language, we've come up with words that mean something. The word word means something and we both understand what it means. In the same way, the genome has come up with a language so that proteins can talk to each other and can talk with DNA.”
For each patient, you can measure their burden of dysregulation in every gene in each pathway using their own genome, then create a modular combination of therapeutics addressing their specific pattern of dysregulation—allowing personalized medicine at scale.
“I can take your own genome. Add up all of the burden that you have in terms of dysregulation of every one of the genes in each of these pathways. And say, okay, well, I need to alter your cholesterol metabolism in this way. And your microglial inflammation in that way. And for each of those, we can now have a modular combination that allows us to create for every patient their own pill.”
The biggest impact of AI yet will be in understanding biology and medicine, and in dramatically, fundamentally changing the human condition—far more significant than current applications in healthcare like language model-based assistants.
“The biggest impact yet will be in understanding biology. And in understanding medicine. And in dramatically, fundamentally changing the human condition. A lot of the early applications that we see now of AI in healthcare are basically just fancy versions of assistants.”
Having pure specialists from different fields collaborate under one roof is more valuable than having pre-mixed interdisciplinary scientists—pure physicists, mathematicians, biologists, chemists, and experimentalists working together leverage diverse thinking.
“I don't want just interdisciplinary scientists all coming to my lab. No, I want the pure physicist, and I want the pure mathematician, and I want the pure biologist, and I want the pure chemist, and I want the pure experimentalist, all working together under one roof. Because diversity is the power of humanity.”
There are two levels of miscommunication: one is misspeaking (formulating your idea imperfectly) and the other is mishearing (misinterpreting what you heard); both are sources of creative divergence because the listener's misinterpretation of your words can lead to genuinely interesting ideas you would not have thought of yourself.
“there are two levels of a misunderstanding. There's one of you misspeaking and another mishearing or mismodeling or what have you... there's something creative in that... there's how I misspeak and how you mishear what I say. And both of them are part of the creative process.”
Research meetings involve brainstorming where students formulate ideas and the group misunderstands them, then collectively explores tangents that might be more interesting than the original direction—this is part of the creative process.
“A student will start saying something. And as they start saying something, every one of us is thinking they're like, we're taking the same concepts and like spinning them in our heads. And I will start understanding the beginning of their sentence. And I will go off on my own tangent... sometimes I will be so enthralled with what I'm saying... okay, I didn't exactly hear what you said, but here's what I think you said.”
At some point, if we don't understand biology when we have this complete picture of disease mechanisms, proteins, chemicals, patients, environmental variables, and measurements, then it's our fault—we have more technologies, data, and facets of function than ever wished for.
“At some point, it becomes so complete and so complex of a picture that if we don't understand it at this point, it's our fault. We basically have more technologies and more data and more facets of function than we ever wished for.”
Kellis's lab has generated more single-cell human brain data from neurodegenerative and psychiatric disorders than any other computational lab in the world, by partnering with clinical experts and experimentalists to create maps of neurological diversity.
“my lab has probably generated more single cell human brain data in dozens of different neurodegenerative and psychiatric disorders. And neurodevelopmental disorders than any other lab in the whole world. And we are a computation lab.”
The formula 'effort equals interest times enjoyment; skill equals talent times effort; achievement equals skill times effort; and effort counts twice' explains how effort is multiplicative—it builds skill which when combined with effort produces achievement that feeds back into enjoyment.
“right above the camera over there, it says, effort equals interest times enjoyment. Skill equals talent times effort. Achievement equals skill times effort. And effort counts twice... your achievement is your skill that you build from your effort times that effort. So basically, effort along with talent gives you skill, but skill again along with effort gives you achievement.”
When encountering something in another field that doesn't make sense, the difficulty likely reflects a gap in one's own understanding of human cognition, and trying to understand that challenging concept trains cognition in new ways.
“when you see something in another field that doesn't make sense, chances are there's some aspect of human cognition that you will be training by trying to understand that.”
The first biology department was created at MIT, before which there were only specialized fields like zoology, botany, and virology; the concept of a unified 'biology' is recent and based on the genome as the unifying principle.
“The first biology department was at MIT. Before that, there was zoology, botanology, virology, and so on and so forth...the concept of biology, of the unification of everything alive, is something that is a very recent concept”
Teaching students to explain results to the smartest physicist on the planet (extraordinarily intelligent but not in their field) is more effective than explaining to a five-year-old—it assumes infinite intelligence and zero field knowledge.
“I tell my students every time is write your computational biology results as if you're speaking to the smartest physicist on the planet. Namely, somebody who's extraordinarily smart. But not in your field. But not in your field. Right. Interesting. I say assume infinite intelligence, zero knowledge.”
Forcing everyone to learn math and sports is misguided—specialization is what makes society work; people should be allowed to excel in their domains rather than being forced to generalize across all domains.
“In today's society, we're basically constantly trying to force everybody to learn math. Why? Why? We don't need everybody to learn math. We're forcing everybody to do sports. Why? Not everybody needs to do sports. I think it's okay to do specialization. That's what made our society work. Like the concept that every one of us has to be good at everything is ridiculous.”
The tradeoff for genetic code recoding is isolation—the organism becomes isolated from the broader biological community and must operate on its own, losing the benefits of horizontal gene transfer and compatibility.
“There must be some trade-off, because otherwise there'd be more. Okay, so what's the trade-off? So, the trade-off is, of course, isolation. You're now on your own. You're now doing your own thing.”
Word meaning in language depends on context—'apple' in fruit basket context is different from 'apple' in computer context, just as pixels in images take meaning from surrounding pixels, and atoms take meaning from neighboring atoms.
“The word apple in the context of a fruit basket is very different than the word apple in the context of a PC or a Macintosh. So the way that our brain understands these words individually has some type of projection, and these words in context can shift these projections. And that concept of words taking meaning with other words around them is identical to pixels in an image taking meaning based on the pixels around them, and atoms in a chemical taking meaning with the atoms around them.”
Language is not just a process of transmission but of creation and excavation—the act of formulating ideas into language changes and creates new ideas.
“I argue something that's akin to the opposite of that, that language is not only a process of transmission, but it's also of creation and excavation.”
By building a bridge and fort as separate Lego pieces (rather than rebuilding them each time), you can reuse them as building blocks—similarly, biological systems get primitives like 'mitochondria' or 'eukaryote' and can use them as building blocks for further evolution without waiting another billion years.
“You build a bridge in Lego and you also build a fort, a tower... the next day you start over and you build the bridge from scratch. But what if you could build a bridge again as if the bridge was its own Lego piece... So that's what we do with ideas because when we hear the word, when we hear about plant, we don't have to reconstruct plant... we just got plants now... For life, you don't have to wait another billion years for a eukaryote to develop. You all of a sudden have life that has mitochondria in it already.”
The human brain is constrained by emotional systems, limbic systems, fear, fight-or-flight, neurotransmitters, brain waves, and hormonal interactions—these could be viewed as bugs or as features enabling the brain to escape local optima and achieve novel solutions.
“The brain is not just subject to traditional electric-based computation. It's also, of course, subject to neurotransmitters and brain waves and hormonal interactions... You can think of them as features or bugs. You could basically say, oh, we have this extraordinarily beautiful cognitive brain, but unfortunately it is influenced by all of these other things. Or you could think of them as features.”
Teaching conversation is like tennis where players exchange balls, but sometimes you hit the ball into interesting parts of the woods rather than back to the player, and then you must explore that interesting area together—this lateral exploration creates value.
“Imagine two people are playing tennis, which is what we think a conversation is supposed to be. But sometimes you can hit the ball instead of toward the person over there into an interesting part of the woods. Now you have to go fetch the ball. You would never have gotten there if you were just exchanging it with one another.”
Students should build foundational knowledge across multiple disciplines and learn to think in every possible way different fields of science have embraced—this interdisciplinary thinking is what enables the unification happening across all fields.
“Build your foundational knowledge, build your foundational understanding of the world, and go and study all of these different disciplines. Soak them up, train your brain to think in every single possible way that different fields of science have embraced. And the reason why I'm saying that is because you will need it. There's this unification that's happening across all of these different disciplines right now.”
Language evolved with limitations of human cognition—language became complex only up to the limit of human comprehension, constrained by evolution and needing to develop the brain from a single cell through developmental programs based on prior evolutionary stages.
“Language is something that evolved by humans talking to each other. It evolved with the limitations of our own evolved brains. So if we humans couldn't understand language, language would not be as it is now. It would be simpler. So language evolved in complexity up until our own comprehension.”
Biology creates niches and shapes those niches, and adapts to niches, making biology in thermal vents fundamentally different from biology in the Sahara Desert and Antarctic ice niches.
“Biology is very different. Biology in, I don't know, the thermal vents of the bottom of the ocean is very different than the biology in the Sahara Desert, very different than the biology in, you know, the Antarctic ice niches. Because biology creates niches, and it shapes those niches, and it adapts to niches.”
The digital code of ACGT is transmitted unaltered from generation to generation—mistakes are replicated as new normal A or T, not blended into something between; inheritance is completely discrete while interpretation of DNA in somatic cells gives rise to complex phenotypes.
“The beauty of the digital code of ACGT is that it is transmitted unaltered from generation to generation. In other words, a thousand generations later, it's not that the A has now diffused into some, you know, barely readable form of A. Every generation is replicated as an A.”
The genome provides initial unification of biology through the central dogma: DNA makes RNA makes protein, revealing a fundamental principle across all life despite vast differences between organisms.
“The genome brings that initial unification of biology, because of the central dogma of biology, DNA makes RNA makes protein. So this concept that even though some organisms are, you know, animals, others are plants, others are viruses and bacteria, there is still some fundamental principle that biology can reveal.”
Exponentiation is repeated multiplication, multiplication is repeated addition, addition is the successor function—you can build complex operations from simpler ones, and understanding the next level requires understanding the foundation.
“We're learning exponentiation with my kids right now. And I explained to them that exponentiation is repeated multiplication. Multiplication is repeated addition... addition is repeated next... successor function.”
In convolutional networks, different parts of an image can activate different primitive representations (diagonal edges, eyes, etc.), and emphasizing these representations through learning creates emergent higher-level features—this is analogous to how Google's DeepDream generated trippy psychedelic images.
“You're sharing parameters across many parts of your image. That you're basically learning these convolutional filters at the lowest level, that are basically learning how to recognize edges. And then at the level above that, you're building on the representations of the layer before, recall 10 years ago or so, when Google Dream or so came out, something like that. And there was these trippy psychedelic images, where a dog became eyes, and they were all eyes for 10 seconds.”
Great ideas spread horizontally across the world through trade and communication—Mesopotamian ideas spread globally, Chinese ideas spread across continents separated by Himalayas, enabling rapid acceleration of civilization.
“A great idea in Mesopotamia would basically spread across the world. A great idea in China would spread, you know, perhaps separated by the Himalayas... the trade routes of being able to sort of take silk and, you know, trade it with glass, or you name it, basically means that you are now exchanging information much, much more.”
Meaning in non-coding DNA arises from the surfaces and landing sites that DNA code provides to proteins—regulators bind from the outside to recognize sequence patterns from the sides of bases, not the backbone.
“The meaning arises from the surfaces, the landing sites, if you wish, that every piece of DNA code provides to the corresponding proteins. So, DNA is a double helix. The bases, A, C, G, T, are basically stacked in pairs... When a protein binds this ladder, it binds from the outside. It binds the atoms that are facing outside.”
Modern societies take care of people regardless of fitness—helping those without good eyesight, those who can't run, those with any disability—allowing humans to excel in diverse directions rather than being optimized for single traits.
“We as a society are there to help. And the beauty of that is that you can now allow humans to excel in thousands of different directions. And basically, you can have someone who's extraordinary at physics, but can't cook their own meal. No problem.”
Mitochondria were originally free-living protobacteria that were engulfed by proto-eukaryotic cells, froze their genome in time with their own translation table, and became the core of energy metabolism in eukaryotes—an example of vertical inheritance of an entire species incorporation.
“Mitochondria were engulfed very early in evolution. They were some protobacterium, if you wish. And it was engulfed by another protoucariot that basically then became eukaryote, that basically now has an organelle that initially was a free-living organism and eventually shed most of its genes, except for about 11 genes, that are now coding for the electron transport chain at the very core of energy metabolism in every one of our cells.”
Methylation of cytosines (adding a CH3 group) modifies DNA base behavior—methyl-C is still read as C by most readers but can be recognized selectively by specific regulators, allowing genomes to use methyl-C as a repressive mark to shut off DNA regions.
“A C, most of the time, is a C. But it can also undergo methylation. For example, you can add a CH3 group to your C, and now it's a methyl C. Now, the methyl C, most of the time, will be read as a C. It still binds a G. It still has the three hydrogen bonds. But on the side, it, you know, bulges a little. Now, there are some regulators that will recognize C, some regulators that will recognize methyl C, and some regulators that will recognize either form without problem.”
As children grow in complex environments, brains adapt during early years—neural connections are fixed and pruned by age 12, causing difficulty learning languages after that age because responsiveness to non-native sounds is lost.
“As children grow up in more and more complex environments, their brains are actually adapting to these environments during their early years. Even during gestation, the signals that you are exposed to are altering the neuronal processes that you have. Why is it so hard to learn a language after the age of 12? Because a lot of your neuronal connections have now been fixed. They've been pruned. The sounds that you could hear as a child have now been pruned away because you're not responding to those sounds.”
Evolution tinkers with gene regulation to explore landscapes of shapes, sometimes preserving function, sometimes breaking it, sometimes improving it—whatever code change led to altered function gets selected for or against.
“Evolution, you can think of all of this tinkering, this tuning that evolution does, as basically exploring the landscape of shapes, if you wish. And this landscape of shapes will basically sometimes preserve the function, sometimes break the function, sometimes improve the function. And now, whatever code change led to that change in shape and that change in function will be selected for or against.”
Evolution explains life through natural selection—organisms with traits enabling better reproduction survive; human behavior usually fits this pattern (dogs like humans because we breed them, plants produce fruit for animal dispersal).
“Nearly every other species, you can explain its behaviors as purely natural selection. You can explain it as, you know, dogs are basically being kind to human and we will breed them more. You know, plants are being kind to animals and they will eat them more. They will eat their fruit and poop their seed, you know, to spread the plant.”
Kellis is a computational biologist at MIT and Harvard's Broad Institute who has been piecing together a unification theory across biology, cognition, and AI for decades, with separate research programs now converging together.
“Professor Manolis Kellis, a leader in computational biology at MIT and Harvard's Broad Institute, has spent decades piecing together a new type of unification theory...all of these separate projects in my group, my team has been working on all of these different aspects separately for decades.”
Kellis's lab has generated more single-cell human brain data in dozens of different neurodegenerative and psychiatric and neurodevelopmental disorders than any other lab in the world, partnering with doctors and experimentalists to generate extraordinary maps of diversity.
“My lab has probably generated more single cell human brain data in dozens of different neurodegenerative and psychiatric disorders. And neurodevelopmental disorders than any other lab in the whole world. And we are a computation lab. Why did we do that? We basically partnered with all of these extraordinary doctors, experimentalists, experts in each of these different areas. And we work together to generate this extraordinary map.”
The host channel is called 'Theories of Everything' and explores unification across domains beyond physics (which uses the term for unifying relativity and quantum mechanics) to include biology, philosophy, and logic.
“on this channel it's called Theories of Everything...most people know theories of everything in physics means how do you merge general relativity with the standard model...But then there's also people in logic, people in philosophy, who have their own ideas”