Thrilled to share our latest work, Bio-Babel: a multi-agent framework for native cross-language software reconstruction. We reconstructed 17 visualization and classic single-cell analysis packages from R in Python, spanning foundational visualization tools such as grid/ggplot2 and widely used single-cell tools including Monocle 2/3, tradeSeq, NicheNet, and copykat. The reconstructed stack features native AnnData/scverse integration and agent-readable MCP contracts. Using this stack, an AI agent recovered pancreatic differentiation trajectories and helped reveal defects under graded SWI/SNF loss. Bio-Babel also extends beyond R to Python translation: its C++ reconstruction of UMI-tools reproduced identical barcode and UMI groupings with higher efficiency. Bio-Babel offers a path to preserve classic methods, bring them into modern ecosystems, and transform them into building blocks for agent-driven science. Paper: doi.org/10.64898/2026.08.30.… Website: biobabel.stanford.edu Code: github.com/Bio-Babel Work led by my incredible post-doc Nianping @nianping_liu , contribured by Xuanzhi Chen @ProtectedTrash (who is applying for PhD program, please keep an eye on his application), Miao Cui @miao_cui0330, Xiaoying Liao, Xiaoke Song, Sairam Pantham @spantham1 , and Weize Xu @Nanguage . If you want your package or any classic package to speak a different tongue, please let us know! More below:

We are thrilled to announce the inaugural Stanford Virtual Embryo Challenge at NeurIPS 2026 @StanfordAILab @NeurIPSConf , in collaboration with Laude Institute @LaudeInstitute , UCSD, and Harvard! Join us in building AI models that predict how life takes shape, across space, scale, time, and perturbation: virtualembryo.ai/challenge. We welcome participants from academia and industry, independent researchers, and even AI agent scientists!!! Our vision is inspired by and complementary to the virtual-cell efforts championed by @arcinstitute . But life is more than a collection of individual cells. Why Virtual Embryos? See below 🧵👇

Excited to share Navigo, our first step toward building an AI-powered Virtual Embryo! By integrating flow matching at the population level with RNA kinetics modeling at the molecular level, and learning developmental dynamics from 12.4 million single cells across 43 embryonic time points, Navigo transforms static snapshots into a continuous, generative model of embryogenesis. The model enables: 1. 🧬 Predicting developmental trajectories across the entire mouse embryogenesis 2. 🫀 Enabling disease modeling by mechanistically resolving regulatory networks that distinguish congenital heart disease subtypes 3. 🧪 Zero-shot genetic perturbation prediction and uncovering lineage-specific gene-compensation mechanisms 4. 🔬 Rational cell-fate engineering, exemplified by fibroblast reprogramming analyses, including identifying pro-fibrotic barriers to cardiac fates and evaluating hundreds of pairwise transcription factor combinations for neuronal fate, each consisting of one bHLH factor and one POU factor We hope this represents an important milestone toward predictive, in silico developmental biology, where virtual embryos can help us understand, simulate, and eventually engineer development. A huge congrats to @YiminFanCUHK from Dr. Yu Li's group at CUHK on this awesome work and all members in my lab and collaborators who made this work possible, and especially to @LaudeInstitute for supporting our vision of building AI-native virtual embryos. We also thank @JShendure @CXchengxiangQIU @junyue_cao @malte_spielmann @XingfanH Jana Henck and @coletrapnell for reporting the original studies and for producing the data we used for training and prediction!