{"slug": "towards-a-virtual-embryo-that-can-predict-what-happens-when-genes-change", "title": "Towards a virtual embryo that can predict what happens when genes change", "summary": "A paper submitted to arXiv on 29 October 2026 introduces lineage inpainting, a generative modeling approach that uses tree-structured transformers and stochastic interpolants to model cell state dynamics across lineage trees. Applied to a lineage-traced transcriptional atlas of mouse embryogenesis, the method recovered dynamics not resolved by gene expression alone, including multiple transcriptional convergence events, and its simulated counterfactuals agreed in many cases with observed developmental phenotypes, including a single-cell measurement of the Tbx5 knockout response. The authors report that lineage information provides a rich source of context for learning multicellular dynamics, despite the model being fit only on unperturbed development.", "body_md": "# Quantitative Biology > Quantitative Methods\n\n  [Submitted on 29 Oct 2026]\n\n# Title:Learning developmental dynamics from lineage context\n\n[View PDF](https://arxiv.org/pdf/2610.40447)\n\n[HTML (experimental)](https://arxiv.org/html/2610.40447v1)\n\nAbstract:A central goal of biology is to predict the dynamics of multicellular systems. Lineage context (the states of a cell's sisters, cousins, etc.) could inform these predictions, but how best to leverage it remains unclear. Here we introduce lineage inpainting, a generative modeling approach that uses lineage context with tree-structured transformers and stochastic interpolants to model cell state dynamics across lineage trees. Applied to a lineage-traced transcriptional atlas of mouse embryogenesis, it recovers dynamics not resolved by gene expression alone, including multiple transcriptional convergence events where distinct lineages are drawn to a common state. Despite being fit on unperturbed development, lineage inpainting can generate hypotheses about perturbation responses by simulating counterfactuals. These simulations agree in many cases with observed developmental phenotypes, including a single-cell measurement of the Tbx5 knockout response. Our results show that lineage information provides a rich source of context for learning multicellular dynamics.\n    \n\n### Current browse context:\n\nq-bio.QM\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/towards-a-virtual-embryo-that-can-predict-what-happens-when-genes-change", "canonical_source": "https://arxiv.org/abs/2610.40447", "published_at": "2026-10-31 11:36:28+00:00", "updated_at": "2026-10-31 11:49:47.058321+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "generative-ai"], "entities": ["arXiv", "Tbx5"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/towards-a-virtual-embryo-that-can-predict-what-happens-when-genes-change", "markdown": "https://wpnews.pro/news/towards-a-virtual-embryo-that-can-predict-what-happens-when-genes-change.md", "text": "https://wpnews.pro/news/towards-a-virtual-embryo-that-can-predict-what-happens-when-genes-change.txt", "jsonld": "https://wpnews.pro/news/towards-a-virtual-embryo-that-can-predict-what-happens-when-genes-change.jsonld"}}