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categories: ["foundations", "genetics"]categories: ["foundations", "genetics"]tags: ["machine learning", "systems biology", "simulation", "computational modelling", "single cell", "foundation models"]tags: ["machine learning", "systems biology", "simulation", "computational modelling", "single cell", "foundation models"]summary: "Computational models that simulate a cell's molecular behaviour, from complete cell cycles of minimal bacteria to learned predictions of how human cells respond to perturbation."summary: "Computational models that simulate a cell's molecular behaviour, from complete cell cycles of minimal bacteria to learned predictions of how human cells respond to perturbation."updated: "2026-10-27"updated: "2026-10-29"humanEvidence: "Complete whole-cell models exist only for minimal bacteria; learned models of human cell lines predict part of the transcriptional response to gene silencing, and none is validated as a model of a whole human cell."humanEvidence: "Complete whole-cell models exist only for minimal bacteria; learned models of human cell lines predict part of the transcriptional response to gene silencing, and none is validated as a model of a whole human cell."access: "Most models and datasets are public, including Recon3D, Geneformer, scGPT and Arc's State; commercial world models are proprietary and released as previews to selected academic and industry users."access: "Most models and datasets are public, including Recon3D, Geneformer, scGPT and Arc's State; commercial world models are proprietary and released as previews to selected academic and industry users."reversibility: "context"reversibility: "context"lines 30–37 → 30–3715 unchanged lines not shown
}}`````` **Virtual cell models** are computational systems that represent what happens inside a cell — DNA,**Virtual cell models** are computational systems that represent what happens inside a cell (DNA,RNA, proteins, regulatory networks and the processes they support — closely enough to predict itsRNA, proteins, regulatory networks and the processes they support) closely enough to predict itsbehaviour under a condition nobody has tested. Two lineages carry the name. Mechanistic whole-cellbehaviour under a condition nobody has tested. Two lineages carry the name. Mechanistic whole-cellmodels write down every reaction and integrate them forward in time; learned models are trained onmodels write down every reaction and integrate them forward in time; learned models are trained onmillions of measured single cells and asked to predict the next measurement. A complete whole-cellmillions of measured single cells and asked to predict the next measurement. A complete whole-celllines 42–48 → 42–484 unchanged lines not shown
A mechanistic whole-cell model aims to account for every annotated gene function in an organism atA mechanistic whole-cell model aims to account for every annotated gene function in an organism atonce. The 2012 model of *Mycoplasma genitalium* split the cell into 28 submodels covering 28once. The 2012 model of *Mycoplasma genitalium* split the cell into 28 submodels covering 28processes, spanning 401 genes, 722 compounds and 1,857 reactions, and let each submodel use whateverprocesses, spanning 401 genes, 722 compounds and 1,857 reactions, and let each submodel use whatevermathematics suited it — flux balance for metabolism, stochastic simulation for transcription — withmathematics suited it (flux balance for metabolism, stochastic simulation for transcription), withthe submodels resynchronised at short time steps.[^karr2012] The virtue is interpretability: everythe submodels resynchronised at short time steps.[^karr2012] The virtue is interpretability: everyprediction traces back to a stated mechanism, and a wrong prediction points at a wrong parameter.prediction traces back to a stated mechanism, and a wrong prediction points at a wrong parameter.The cost is that the parameters have to come from somewhere. That model needed roughly 1,800 ofThe cost is that the parameters have to come from somewhere. That model needed roughly 1,800 oflines 72–82 → 72–8223 unchanged lines not shown
```timeline```timeline[[ { "year": "1996–1999", "title": "E-Cell", "text": "Masaru Tomita's group at Keio University builds a simulation environment and assembles a hypothetical self-sustaining cell from 127 genes selected out of the Mycoplasma genitalium genome." }, { "year": "1996-1999", "title": "E-Cell", "text": "Masaru Tomita's group at Keio University builds a simulation environment and assembles a hypothetical self-sustaining cell from 127 genes selected out of the Mycoplasma genitalium genome." }, { "year": "2012", "title": "The first whole-cell model", "text": "Karr and colleagues in Markus Covert's laboratory publish a model of Mycoplasma genitalium covering every annotated gene function, and use it to direct experiments that recovered previously unmeasured kinetic parameters." }, { "year": "2012", "title": "The first whole-cell model", "text": "Karr and colleagues in Markus Covert's laboratory publish a model of Mycoplasma genitalium covering every annotated gene function, and use it to direct experiments that recovered previously unmeasured kinetic parameters." }, { "year": "2018", "title": "Human metabolism reconstructed", "text": "Recon3D assembles a genome-scale account of human metabolic reactions with associated protein structures, extending an effort begun a decade earlier." }, { "year": "2018", "title": "Human metabolism reconstructed", "text": "Recon3D assembles a genome-scale account of human metabolic reactions with associated protein structures, extending an effort begun a decade earlier." }, { "year": "2022", "title": "A minimal cell in silico", "text": "A whole-cell kinetic model of the 493-gene synthetic bacterium JCVI-syn3A reproduces how the cell balances metabolism, gene expression and growth across a cycle." }, { "year": "2022", "title": "A minimal cell in silico", "text": "A whole-cell kinetic model of the 493-gene synthetic bacterium JCVI-syn3A reproduces how the cell balances metabolism, gene expression and growth across a cycle." }, { "year": "2023–2024", "title": "Single-cell foundation models", "text": "Geneformer and scGPT are trained on tens of millions of transcriptomes and released, moving the field's centre of gravity from equations to learned representations." }, { "year": "2023-2024", "title": "Single-cell foundation models", "text": "Geneformer and scGPT are trained on tens of millions of transcriptomes and released, moving the field's centre of gravity from equations to learned representations." }, { "year": "2024", "title": "The AI virtual cell proposed", "text": "A 42-author perspective in Cell sets out what an AI Virtual Cell would have to be: universal representations across scales, queried through virtual instruments, judged against experiment." }, { "year": "2024", "title": "The AI virtual cell proposed", "text": "A 42-author perspective in Cell sets out what an AI Virtual Cell would have to be: universal representations across scales, queried through virtual instruments, judged against experiment." }, { "year": "2025", "title": "Benchmarks and data at scale", "text": "The Arc Institute releases State and launches the Virtual Cell Challenge; the Chan Zuckerberg Initiative starts the Billion Cells Project with 10x Genomics and Ultima Genomics." }, { "year": "2025", "title": "Benchmarks and data at scale", "text": "The Arc Institute releases State and launches the Virtual Cell Challenge; the Chan Zuckerberg Initiative starts the Billion Cells Project with 10x Genomics and Ultima Genomics." }, { "year": "2026", "title": "A bacterial cell cycle in four dimensions", "text": "A spatial and kinetic model simulates the roughly 100-minute cycle of JCVI-syn3A end to end, including growth and division." }, { "year": "2026", "title": "A bacterial cell cycle in four dimensions", "text": "A spatial and kinetic model simulates the roughly 100-minute cycle of JCVI-syn3A end to end, including growth and division." },lines 157–164 → 157–16474 unchanged lines not shown
**Parameters nobody has measured.** The mechanistic route needs rate constants, concentrations and**Parameters nobody has measured.** The mechanistic route needs rate constants, concentrations andlocalisations for every reaction. For bacteria these were assembled from decades of literature. Forlocalisations for every reaction. For bacteria these were assembled from decades of literature. Fora human cell most of them have never been measured in any cell, let alone the one being modelled —a human cell most of them have never been measured in any cell, let alone the one being modelled.the same constraint that limits [[human-digital-twins|person-scale models]] one level up.The same constraint limits [[human-digital-twins|person-scale models]] one level up. **A noisy readout.** Single-cell RNA sequencing is the field's main training signal and it is noisy.**A noisy readout.** Single-cell RNA sequencing is the field's main training signal and it is noisy.Differences a model learns may be biology or may be artefacts of how the sample was handled, andDifferences a model learns may be biology or may be artefacts of how the sample was handled, andlines 193–199 → 193–19928 unchanged lines not shown
would have to demonstrate: representations that transfer across scales, a way to interrogate themwould have to demonstrate: representations that transfer across scales, a way to interrogate themthat resembles an instrument, and evaluation against experiment rather than against held-outthat resembles an instrument, and evaluation against experiment rather than against held-outdata.[^bunne2024] Much of what is marketed under the name meets none of those conditions, and thedata.[^bunne2024] Much of what is marketed under the name meets none of those conditions, and thelabel has spread faster than the capability behind it — a pattern already familiar fromlabel has spread faster than the capability behind it. The same pattern is familiar from[[ai-drug-discovery]], where the category has no regulatory definition and therefore no denominator.[[ai-drug-discovery]], where the category has no regulatory definition and therefore no denominator. The plausible near-term uses are narrower than the name suggests and real. Triaging whichThe plausible near-term uses are narrower than the name suggests and real. Triaging whichlines 215–254 → 215–25315 unchanged lines not shown
applies weakly here, since a model of how a cell responds to a perturbation is not a design tool forapplies weakly here, since a model of how a cell responds to a perturbation is not a design tool forpathogens, though the distinction narrows as the models grow.pathogens, though the distinction narrows as the models grow. The 2026 funding brings these questions to the data rather than the models. Its stated rationale isThe 2026 funding raises these questions about the data rather than the models. Its stated rationalethe proponents' side of the argument set out below, that data is the binding constraint, put byis the proponents' side of the argument set out below, that data is the binding constraint, put byBiohub's head of science as a need for "orders of magnitude more data than exists today".[^biohub2026a]Biohub's head of science as a need for "orders of magnitude more data than existsThat is a premise, not a finding. Neither announcement names a licence beyond calling the result antoday".[^biohub2026a] That is a premise rather than a finding. Neither announcement names a licenceopen resource, gives a date for releasing data, describes how consent and access will be governedbeyond calling the result an open resource, gives a date for releasing data, describes how consentfor the human donors whose cells and records are pooled, or mentions misuse.[^biohub2026a][^biohub2026b]and access will be governed for the human donors whose cells and records are pooled, or mentionsWhat the corporate funders, one of them Meta, receive was explained to Reuters rather than in themisuse.[^biohub2026a][^biohub2026b] What the corporate funders, one of them Meta, receive wasannouncements: data paid for by commercial funders is held for an embargo period in which they canexplained to Reuters rather than in the announcements: data paid for by commercial funders is heldwork on it before it becomes a public resource, while the government-funded work carries no suchfor an embargo period in which they can work on it before it becomes a public resource, while therestriction. Biohub's head of science also told Reuters he expected a first dataset in about a yeargovernment-funded work carries no such restriction. Biohub's head of science also told Reuters heand accurate predictive models within five years.[^reuters2026]expected a first dataset in about a year and accurate predictive models within fiveThe gaps are the questions that attach to any pooled biomedical resource: who controls access, whetheryears.[^reuters2026] The gaps are the questions that attach to any pooled biomedical resource: whoa donor can withdraw, whether data held in federal repositories stays available as policy changes,controls access, whether a donor can withdraw, whether data held in federal repositories staysand whether models that predict how a person's cells respond could informavailable as policy changes, and whether models that predict how a person's cells respond could[[genetic-discrimination|decisions by insurers or employers]]. The embargo also sharpens theinform [[genetic-discrimination|decisions by insurers or employers]]. The embargo also sharpens theconcentration described above: the companies that pay for the largest datasets are the first toconcentration described above: the companies that pay for the largest datasets are the first totrain on them.train on them. ## Outlook## Outlook The test the field has set itself is clear enough to fail. A virtual cell would predict aThe field has set itself a test that a model can clearly fail. A virtual cell would predict aperturbation's effect in a cell type it was not trained on, and the prediction would hold whenperturbation's effect in a cell type it was not trained on, and the prediction would hold whensomeone ran the experiment. Nothing published meets that standard, and the honest reading of thesomeone ran the experiment. Nothing published meets that standard, and the 2025 benchmarks indicate2025 benchmarks is that scale alone has not yet produced it. Proponents argue that data is thethat scale alone has not yet produced it. Proponents argue that data is the binding constraint andbinding constraint and that billion-cell corpora will change the picture, the premise the 2026 Virtual Biologythat billion-cell corpora will change the picture, the premise the 2026 Virtual Biology InitiativeInitiative commitments rest on; sceptics point out thatcommitments rest on. Sceptics point out that the same argument was made at each previous order ofthe same argument was made at each previous order of magnitude, and that a model trained only onmagnitude, and that a model trained only on transcriptomes may be missing variables rather than data.transcriptomes may be missing not data but variables. Two harder questions sit behind the benchmark. Whether cells are predictable at all in the relevantTwo harder questions sit behind the benchmark. Whether cells are predictable at all in the relevantsense is not settled: a cell is a stochastic system, and the useful prediction may be a distributionsense is not settled: a cell is a stochastic system, and the useful prediction may be a distributionrather than a state. And a model of a cell in isolation says little about cells in tissue, whererather than a state. And a model of a cell in isolation says little about cells in tissue, wheresignalling between neighbours does much of the work — the reason a virtual cell is not a step on asignalling between neighbours does much of the work. For that reason a virtual cell is not a step onpath that ends at [[whole-brain-emulation]], despite the shared vocabulary. Ona path that ends at [[whole-brain-emulation]], despite the shared vocabulary. On[[technology-readiness-level|readiness]] the mechanistic branch is furthest along and applies to[[technology-readiness-level|readiness]] the mechanistic branch is furthest along and applies tobacteria; the branch that applies to human cells is earlier than its funding implies. The claim thatbacteria; the branch that applies to human cells is earlier than its funding implies. The claim thatprogress toward [[artificial-general-intelligence|more general AI systems]] will carry biology alongprogress toward [[artificial-general-intelligence|more general AI systems]] will carry biology alongremoved, struck through added, underlinedLine numbers count the serialised markdown of each revision, frontmatter included.
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