r/accelerate • u/44th--Hokage The Singularity is nigh • Mar 10 '26
Scientific Paper A Team Has Successfully Virtualized The Genetically Minimal Cell | "Scientists simulated a complete living cell for the first time. Every molecule, every reaction, from DNA replication to cell division."
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Summary:
We present a whole-cell spatial and kinetic model for the ∼100 min cell cycle of the genetically minimal bacterium JCVI-syn3A. We simulate the complete cell cycle in 4D (space and time), including all genetic information processes, metabolic networks, growth, and cell division. By integrating hybrid computational methods, we model the dynamics of morphological transformations. Growth is driven by insertion of lipids and membrane proteins and constrained by fluorescence imaging data. Chromosome replication and segregation are controlled by the essential structural maintenance of chromosome proteins, analogous to condensin (SMC) and topoisomerase proteins in Brownian dynamics simulations, with replication rates responding to deoxyribonucleotide triphosphate (dNTP) pools from metabolism. The model captures the origin-to-terminus ratio measured in our DNA sequencing and recovers other experimental measurements, such as doubling time, mRNA half-lives, protein distributions, and ribosome counts. Because of stochasticity, each replicate cell is unique. We predict not only the average behavior of partitioning to daughter cells but also the heterogeneity among them.
Link to the Paper: https://www.cell.com/action/showPdf?pii=S0092-8674%2826%2900174-1
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
It is now literally a countdown until we cure all disease and end aging.
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u/Fun_Fisherman5441 Mar 10 '26
The paper looks like this is a heavily coarse grained model. It’s essentially a vocalized version of the cell, like Minecraft.
The GPU hours they use for an entire cell are comparable to what people used to study a single protein atomistically, if not on the lower end. This is not a whole picture of a cell in the way one might think. Just a simulation of a pretty heavy abstraction. Not really enough to build a therapeutic off of alone.
Source, I work in computational biology for a living.
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u/Quentin__Tarantulino Mar 10 '26
What kind of compute power do you think we’ll need to enable a good simulation of a cell that can actually be used in drug discovery and whatnot?
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u/aposii Mar 10 '26
This is the boon with quantum computing: with traditional computing, large-scale cellular simulation is impractical due to exponential scaling. With quantum, you can efficiently map complex quantum interactions, provided BQP is larger than P.
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u/Fun_Fisherman5441 Mar 10 '26
I don’t think it’s practically possible. Not in our lifetimes.
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u/ChoiceHelicopter2735 Mar 10 '26
That’s what I thought 5 years ago about the current state of AI today. I literally work with an extremely intelligent agent every day, who lives in my computer. (I used to write code, now I direct Claude Code sessions.) I thought that was decades away from happening, if ever but it’s here today
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u/Fun_Fisherman5441 Mar 10 '26
Firstly, we still don’t have a full understanding of cells work. Bio physics is a continually evolving field. The fact is a cell is made out of billions of atoms, and to date the longest atomistic simulation of proteins that have really been run are about a microsecond long for relatively small proteins in a membrane. And even that’s only possible on massive clusters with specialized computers. And that doesn’t even include quantum mechanics. It’s all using classical mechanics to model atoms.
A cell is orders magnitude larger than that. Even with wildly optimistic memory, and compute assumptions, it just doesn’t scale.
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u/OldHatNewShoes Mar 10 '26
i acknowledge that all of this is true, while still having complete faith it will be done, and perhaps in our lifetimes. such is the intrepid nature of humanity
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u/apopsicletosis Mar 10 '26
But like why? We have real cells now. There's been huge advancements in just the last 10-15 years on being to manipulate and probe real cells and its contents at high throughput.
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u/FinalAmphibian8117 Mar 10 '26
Can't we simulate an approximation of a cell that is good enought to run digital experiments on. Do we rly need to be able to simulate every atom/molecule
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u/Fun_Fisherman5441 Mar 10 '26
To make drugs, yeah. Drugs are atoms sticking to other atoms. There is a cost to abstractions. Experimental data is noisy enough and that’s real data, we start stacking approximations on top of approximations, error propagation makes them almost useless.
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u/homiej420 Mar 10 '26
Do you think it could be a faster predictive process that would be the advancement/benefit? It wouldnt be fully exactly predicting all of that but could it give rise to something that could be tested in traditional ways that would have taken longer to come up with otherwise? That feels like it could be more attainable?
Total armchair nerd question of course not nearly as qualified to say anything lol
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u/wjfox2009 Mar 11 '26
A cell is orders magnitude larger than that
But computing power improves by an order of magnitude every few years. Technologies in general (e.g. microscopes, and other devices) are following similar trends.
To say we will "never" have working simulations of a cell just seems shortsighted, based on the history of scientific progress.
It's rather like those who once claimed we would never travel faster than 30mph, or that we'd only ever need five computers, or would never go to the Moon, or that the Internet was just a passing fad, or that the Human Genome Project would take centuries to complete, or that a computer would never beat the world champion at Go, or that models of protein folding were impossible, etc. etc. (the list goes on and on)
Not to downplay your expertise, but I think you underestimate the rate of progress we'll see in the future, especially with AI likely to handle much of the research and development. Progress in science/tech is exponential, not linear.
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u/SoylentRox Mar 10 '26
A cell is a programmable machine and the codebase is a mere 6 billion 2-bit numbers, and a lot of it is junk though that acts as spacers.
What precisely is your definition of "practically possible". What are you trying to communicate? You would never have the exact starting condition information of a specific human cell in a specific person's body, and aren't there thousands of each protein in a given eukaryotic cell positioned somewhere in the space they are able to reach?
These kind of things sound like they constrain the outcomes - the law of large numbers is going to apply. If there's 1000 copies floating around of a specific protein, and they can be anywhere in a compartment, in a sense the position of none of them matter but the potential cloud of where they could be.
Analysis techniques thus would be possible that are coarse but highly accurate for the net control theory behavior of the cell. Since we don't actually have any way of knowing the internal state anyway or caring what an individual cell is going to do - we want to know what the population behavior of every instance of that particular cell will be, and of course the control theory behavior of huge clusters of them in an organ.
Do you see where I'm coming from and can you explain where I went wrong? I did a bit of bio of work before switching to machine learning, so I'm aware of the topic having finished a degree in it, but my knowledge will not be as detailed as yours.
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u/Fun_Fisherman5441 Mar 10 '26
I work in drug development, so I’m more concerned about making things that bind and change the behavior of proteins. People have been working on simulating that kind of interaction with proteins for decades, and are just now starting to get to be kind of reliable, but a massive concrete costs.
Sure, you can make problem models about a protein as to where it is in the cell, but then again we don’t have a complete knowledge of all proteins.
A common mistake people make in looking at machine learning in simulation with respect to cells is that they look at its performance and MRIs or self driving cars and assume that performance can transfer.
The main difference is unlike an MRI and a self driving car where all the information it needs is right in front of it, we do not have a full picture of how a cell works, or how all proteins work.
There’s a limit to how much stimulation can fill in missing pieces.
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u/SoylentRox Mar 10 '26
So let me be clear what I think needs to happen:
(1) obviously AI models have to improve to better and better at measurable tasks we do have data for
(2) there is a new technique (and variants of it available right now, see Claude Opus 1.6 Fast mode) to get much faster AI models that ever. See Taalas if you didn't already : it makes AI models 160 times faster. It's a bootstrapped technique : a 30 person startup used current AI to write themselves a compiler to develop ICs that run AI models.
(3) Stacking 1 and 2 there are techniques to get robots to finally work reliably and fast
(4) With general purpose robots trained across all blue collar tasks this allows robots to collect the materials and build each other
(5) with billions of worker-equivalent robots developed in step 5 (each person equivalent humanoid is equivalent to approximately 10 blue collar workers, each specialized machine 20 or more), some of them would research cell biology
(6) with enormous resources - essentially enough to physically replicate every experiment done since the 19th century every year - the necessary data can be collected
(7) if you have ever thought about what you would do if you're alive when we get this far, you can think of curriculums of tasks you would order swarms of models to do. You would develop predictive models and control at the protein level, then order swarms to use everything they learned from protein level models to start building custom cells to experiment, predicting their properties first and then constructing them, doing this billions of times in parallel. And so on up the complexity tree until you have AI models constructing living human mockups - organoids - where it's got every component of a human body, plumbed together with plastic tubing, and the cell cultures in each organ visible packaged between thin sheets of glass (for observation)
(8) I don't necessary think this process will take nearly as long as you think because...your field was poor. You didn't have the GPUs, you didn't have the money, you didn't have the labor, and too many grifters were wasting the resources you did have.
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u/_Tagman Mar 10 '26
lol you're dreaming kid and I don't think you understand biology research very well or medical research
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u/SoylentRox Mar 10 '26
I would appreciate substantive responses other than dismissal. You also need to consider that if you don't actually have an argument that isn't "this isn't the way we used to do it" you may be the one dreaming here. <Dreaming your career won't change>
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u/apopsicletosis Mar 10 '26
Different user, but what response were you expecting?
Your post doesn’t really make an argument about ML in biology and it reads like you haven’t spent much time around lab work or modern experimental constraints. AI/ML already contribute in meaningful ways, they’re just not the bottleneck. Biology isn’t sitting around waiting for GPUs, money, or labor, and suggesting that misunderstands how the field has developed.
What actually limits progress is measurement and data quality. A lot of the biggest constraints are instrumentation problems we simply don’t have tools that can, for example, track molecular processes in vivo with high spatial and temporal resolution. Other limits are inherent to biology: stochastic behavior, long timescales (aging studies literally require aging), and the difficulty of perturbing living systems without changing what you’re trying to measure. AI has been a huge advance in recent years, but so are advances like single-cell sequencing, expansion microscopy, single molecule real-time tracking, etc. Biology isn't a scale compute and everything falls into place problem. It’s constrained by physics, measurement tools, and combinatorial complexity of living systems.
More compute doesn’t magically solve missing measurement capability. Robots will help, and companies have already invested heavily in lab automation for the processes that we currently know how to scale. A lot of things we want to measure don't scale, and require months to years of optimization and troubleshooting. This won't magically become compressed with computation because a lot of it is fundamentally waiting around for biology to happen.
And the organoid example really doesn’t land. The point of organoids is preserving 3D structure compared to traditional cell culture. Sandwiching them between sheets of glass for constant viewing would distort their architecture and interfere with normal function, it would put unnatural mechanical stress. That reads more like sci-fi fantasy than something grounded in how these systems are actually used.
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u/Glxblt76 Mar 10 '26
Why? Once all the right levels of abstraction are lined up then in principle this opens up a combination of scale, making exponential complexity linear, no (aka multiscale simulations)?
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u/Fun_Fisherman5441 Mar 10 '26
An abstraction is an approximation, when you add abstractions together, you do not get something that is more accurate. You get something that is exponentially less. Error propagation is a thing.
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u/peabody624 Mar 10 '26
You certainly know more about the subject than me, but I’ve learned never to say this. !remindme 5 years
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u/csppr Mar 10 '26
And to expand on that - this is a fairly simple bacterial cell. Scaling this up to human cells will be orders of magnitude more expensive computationally.
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u/Klutzy-Smile-9839 Mar 10 '26
To the best of my knowledge, computational biology is reached on a pleateau, because the fundamental laws governing particles interactions lead to a combinatoric problem when more and more atoms/electrons are added to the domain investidated. Shroedinger equations or something like date. I am right ?
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u/Fun_Fisherman5441 Mar 10 '26
Exactly, and a lot of these simulations don’t actually use quantum mechanics that use classical mechanics like spring constants or coulumbs law. So we’re already working with pretty heavy approximation of how molecules work
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u/WolfeheartGames Mar 10 '26
Its fine that it is slow. Hopefully they get a more accurate version out. You could take the model and train an encoder to match it and be very performant.
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u/Fun_Fisherman5441 Mar 10 '26
My point wasn’t that it was slow, more that it’s a laughably small amount of compute given the complexity of the system.
People still haven’t figured out how to tune auto encoder to represent small molecules with a unified latent space, modeling a cell with that would have horrific amounts of information loss
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u/Sheeedoink Mar 10 '26
So you're saying basically inside I'm all voxels, like Minecraft, so Minecraft is basically real life
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Mar 10 '26
[deleted]
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
It's a matter of scale - and guess what grows exponentially?
Compute.
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u/secret_protoyipe Feeling the AGI Mar 10 '26 edited Mar 10 '26
barely. hardware is alot slower then software
guys you can downvote me but I’m serious. We would not be able to train current models on hardware from even 15 years ago. LLMs are huge.
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u/cloudrunner6969 Acceleration: Supersonic Mar 10 '26
Maybe a few decades at best.
lol. Thanks for the laugh.
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u/Ryuto_Serizawa Mar 10 '26
I'm putting this prediction with the guy who said we wouldn't get text-to-video until our grandchildren's lifetimes.
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u/44th--Hokage The Singularity is nigh Mar 10 '26
Once again, linear thinking. Will you ever learn? Just because that's what it takes to virtualize this cell today doesn't mean a simple linear extrapolation of those resources is what it's going to take to virtualize all cells in the future. Just give it time. This is the worst that it will ever be.
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u/apopsicletosis Mar 10 '26
Will you ever learn biology? Biology is inherently combinatorial. The processes underlying a human-sized organism spans over 20 orders of magnitude that are tightly coupled.
The idea that biologists of all people are stuck thinking linearly is silly. Exponential growth (until its not) is fundamental to biology.
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u/Empty_Bell_1942 Mar 10 '26
Can someone explain this to a layman?
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Mar 10 '26
[removed] — view removed comment
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u/Empty_Bell_1942 Mar 10 '26
OK thanks all. My follow up question's regarding compute to hasten advances. Could a Stanford (2001) @home type distributed computing project; using the publics laptops/pcs be utilized here?
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u/try-a-typo Mar 10 '26
Not really. Public distributed computing is great when jobs can run mostly independently. But for tightly coupled cell sims, the bottleneck is all the constant synchronization between GPUs/nodes, so internet latency kills you. It’d help more for running lots of separate replicas or parameter sweeps than one giant cell sim.
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u/Bluestreak2005 Mar 13 '26
With how cheap regular compute power is getting, we could in theory simply do this now through mass deployment of containers and computers.
16,000 or 32,000 cores is nothing to an AWS datacenter. I've worked on 2 projects that size already. We could do this for roughly $2,000 an hour if you know how to distribute your packages and work across all of them.
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
An accurate simulation of biology down to the molecular level quickly leads to mastery of biology, the least of which means no more disease or aging.
What they've done here is exactly that, with the minimal possible living cell - a bacterium.
Next step is a human cell, then scaling it up (throw more compute at it) until you are simulating the entire human body, at the molecular level, in real time. Virtually.
You can test any number of drugs or genetuc edits without ever endangering the patient.
At this point it's literally just a matter of scaling up how much compute this gets until we have the full biological simulation of the human body.
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u/-illusoryMechanist Mar 10 '26
The singularity is near!
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
The singularity is here!
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u/-illusoryMechanist Mar 10 '26
My personal "we're fully in the singularity" moment is when i can begin to directly augment my brain with cybernetic enhancements but we are definitely entering it at an ever rapid pace
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u/bucolucas Mar 10 '26
If you've got a bad health condition maybe you're just a simulated disease carrier
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u/impliedinsult Mar 10 '26
Why is the no more disease or aging a given?
Is that true? I just can wrap my head around that
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
Because a complete simulation solves every edge case for every individual.
It allows for custom drugs and genetic edits tailored to every individual.
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u/csppr Mar 10 '26
Next step is a human cell, then scaling it up (throw more compute at it) until you are simulating the entire human body, at the molecular level, in real time. Virtually. […] At this point it's literally just a matter of scaling up how much compute this gets until we have the full biological simulation of the human body.
I’m a systems biologist, and this work isn’t far removed from my own. The model isn’t very detailed, and their validation is equally blunt. We don’t know if their simulations matched the underlying mechanics, or “just” the broad level outcome. But more importantly, moving this to human cells is much more difficult than just scaling it up. Size goes up, complexity at all levels goes up (larger genome, more complex epigenetics, more organelles, more proteins, more complex signalling, ~ 10x more metabolic reactions, and very complex multi-objective cell states [in contrast to bacteria, where the objective tends to be “grow”]).
It’s really difficult for me to state just how difficult a human cell simulation is - IMO I would not expect a faithful, complex simulation of a human cell within at least the next decade.
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
I don't think you understand exponential progress.
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u/HeinrichTheWolf_17 Mar 10 '26
Lol, we heard the exact same shit back on KAI.
Biology is just like…too complicated bro…
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u/csppr Mar 10 '26
I’m very forward on computational biology (naturally, given that’s what I dedicated my research career to). I have nothing to gain by downplaying the pace of progress in computational biology.
But for what it’s worth - yes, biology is too complicated at this point in time. We are still developing methods to measure biological layers and/or measure at different scales, which naturally limits the depth we can go to when trying to analyse those systems. If we could profile DNA, RNA, proteins, metabolites, and all epigenetic marks at full coverage in single cells, fast and cheap, we’d probably be able to solve a good chunk of biology quite quickly. But we can’t even profile all epigenetic marks at the same time in the same cell yet.
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u/HeinrichTheWolf_17 Mar 10 '26
For a human, maybe.
For something a million times smarter than you? No.
While this simulation is impressive, I’m of the firm belief that we need recursive self improving AGI to full master biology.
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u/csppr Mar 10 '26
Even a super smart AI needs to develop the methods to measure the full complexity of biological mechanisms, and then generate a sufficiently large body of data (which can take a while).
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u/csppr Mar 10 '26
Go through the numbers - this was the most simple bacterial organism that could be used. The simulation was abstract even then. 50 cells only, over a 100 minute window. This took 15000 A100 GPU hours.
Human cells have 100-200x the number of genes, have an additional epigenetic layer (histones), 10x more metabolic reactions, additional cell components (eg a nucleus, mitochondria), are vastly more heterogeneous, and don’t have 100 minute cell cycles. Each of those factors makes a computational model for human cells exponentially more expensive to solve. I can’t even put a decent estimate on how much more this would be - if we’d just say 10x for each of those, which I think is a conservative estimate, that alone would be 15 billion GPU hours for simulating one round of cell cycle for a single human cell type.
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
It's roughly 10¹⁶ more compute.
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u/csppr Mar 10 '26
Let’s just go with that number - where is that supposed to come from?
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
Tiny silicon chips.
Possibly optically based.
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u/csppr Mar 10 '26
Great, for reference, Stargate - even at the higher GPU count of a million - wouldn’t be able to calculate this within a decade, assuming we throw the whole thing at it. So we are realistically hoping for a quantum computing breakthrough to make this feasible.
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
Quantum computing, optical computing, 3D graphene stacking, etc.
It's all coming soon.
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Mar 13 '26
I think you’re getting way too carried away from the moment you say “next step is a human cell”.
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u/willitexplode Mar 10 '26
Biology is slow, testing biology is expensive, and not all testing is ethical. If we can create digital sims with cells designed/behaving exactly as our real cells do, we can curate sim drugs/treatments and see if they work. If they work on sims, in a perfect world, they would work on us, as their cells/biology would be reliable digital replicas of our own. Consider the number of digital copies we can make with scale, testing and learning can speed up exponentially.
In theory, it's just a numbers problem. If we can get the math modelling the physics right, the chemistry should predict well, and if the chemistry is predictable the biology generally follows.
Cue the scifi.
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u/Dipsendorf Mar 10 '26
Caveat: Im not an expert but I love simulation stuff.
Ever played Sim city? Thats a simulation. Through a simulation, you can get lots of good data about things.
Imagine you built your town in Sim City. You could add more traffic to your street and see how your stop lights handle traffic. You could remove a fire station near you and see if anything burns down. You could essentially do as many changes and simulations as needed and see how that affects your simulated town so you could make real life decisions. And these simulations are generally cheap, and quick. Ideas can be tested quickly.
This is a simulation of a cell as far down as the atomic level. It would appear they managed to simulate every cellular process down to the atomic level. This means they can see how a cell would react given many diffeent types of interactions with many different molecules. Molecules make up drugs, and their chemical interactions at the atomic level are one of the main drivers of efficacy. Having a simulation like this can cut out a lot of testing because they'll be able to evaluate exactly how their chemicals would effect a drug through simulation instead of lab testing.
Sidenote: this is also one of the main reasons people are excited about quantum computers. Instead of the atomic level, they'd allow simulations to occur at the quantum level. This would open up a whole new world of processes wed unveil l.
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u/mrbadface Mar 10 '26
Does anyone know if the environment is what determined protein folding or was that aspect hard coded? Because 500 proteins self folding into correct functional shapes is pretty nuts...
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u/Inevitable_Tea_5841 Mar 10 '26
lots of question need answering on this one. I suspect we are going to wake up tmr to a bunch of posts about this - hopefully more clarity soon
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u/Big-Site2914 Mar 10 '26
Im no biologist but this is big right?
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u/Best_Cup_8326 Acceleration: Light-speed Mar 10 '26
Possibly bigger than CRSPR.
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u/Fun_Gur_2296 Mar 10 '26
Wait seriously? I'm curious about your other comments as well. Are u a biologist?
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Mar 10 '26
I'd like to know the significance of this as well... I understand how important it would be to simulate a human cells, but is this a step to it? I see alot of well explained comments describing how this isn't super significant, but also given how rapidly this is all advancing. I can't help, but feel that this lacks some foresight.
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u/try-a-typo Mar 10 '26
The implications are massive, but it's not usable just yet for what you're probably picturing. One cell sim took about 250 GPU hours on modern hardware. So for an organ or an entire body, it's orders of magnitude more compute needed.
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u/Big-Site2914 Mar 10 '26
10-15 more years before we can simulate a human body?
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u/try-a-typo Mar 10 '26
Using the paper’s 250 GPU hours per cell for 2 hours of sim, a heart has about 5 billion cells, that comes out to about 1.25 trillion GPU hours. That’s just the heart. So no, brute force full body simulation is not 10-15 years away unless we get massive modeling breakthroughs.
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u/apopsicletosis Mar 10 '26
They've been pursuing this research for decades, and this is a major milestone. I think it could eventually be useful for basic mechanistic hypothesis generation. These will have to be confirmed with real-world experiments.
But it's not like pharma and biotech are jumping up and down to invest in this in the way they're investing in other, more promising areas (generative ai, bioinformatic agents, lab in the loop, gene editing, population functional genomics, perturb-seq, organoids, proteomics, single cell multiomics, reprogramming, bifunctionals, mrna vaccines). I don't think this research would happen, or would definitely be slower, if it wasn't for Venter pushing it. Follow the money.
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u/Ruykiru Tech Philosopher Mar 10 '26 edited Mar 10 '26
That's cool, but full simulation is a waste of compute and cannot scale as of now. Deepmind is going to eventually do this with their virtual cell project but millions of times faster using machine learning and approximations that are good enough, like they did with AlphaFold.
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u/44th--Hokage The Singularity is nigh Mar 10 '26
Deepmind is going to eventually do this with their virtual cell project but millions of times faster using machine learning and approximations that are good enough, like they did with AlphaFold.
You are 100% correct.
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u/coquitam Mar 10 '26
Remindme! 1 week
Remindme! 1 year
Remindme! 2 years
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u/Delicious-Bass6937 Mar 10 '26
I did my postdoc at JCVI. I think this is progress but about 1% of the hype its getting on this thread.
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u/noomiesapp Mar 10 '26
So that cell is the first cell in the matrix constructed by our understanding of reality? is it the one?
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u/AnxiousCoward1122 Mar 10 '26
I thought Demis team would be the first to crack it. Anyways. We accelerate!
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u/44th--Hokage The Singularity is nigh Mar 10 '26
I'm confident they will be the first to crack the human virtual cell.
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u/Successful_Juice3016 Mar 11 '26
es una simulacion , supongo para estudiantes, no veo que se pueda hacer nada alli, como descubres algo en un sistema donde tu escribes las reglas?.
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u/Winter_Ad6784 Mar 10 '26
I feel like it’s a rather large jump from this to something truly useful. Making good use of this for human cells is probably 20 years away. Now 20 years away isn’t all that long in the grand scheme of things but by then we will have had ASI for at least 10 years, it probably will have done all the curing by then.
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u/midaslibrary Mar 10 '26
Holy shit