昨年出した論文がBiophysics and Physicobiology Editors' Choice Award 2023を受賞しました!
https://t.co/5p2cQVpHcp
Phenotypic systems biology for organisms:Concepts, methods and case studies
表現型システム生物学:概念、方法、ケーススタディ
https://t.co/cuOpsBESp8
より一層励みます。
論文が出ました
Phenotypic systems biology for organisms: Concepts, methods and case studies
表現型システム生物学:概念、方法、ケーススタディ
https://t.co/WAYmUQnYf3
遺伝子中心のシステム生物学を、表現型(形態、行動、生活史など)に拡張する野心的な試みです。新分野の創出を目論みます
PhyloCNN: Improving Tree Representation and Neural Network Architecture for Deep Learning from Trees in Phylodynamics and Diversification Studies
https://t.co/vHIiXVeoq3
The catabolite repression response has been considered to be the hallmark of E. coli’s substrate preferences. A detailed study including over 700 transcriptomes and 43 nutrients now shows, using iModulons, a 4 layered substrate selection logic in E. coli. https://t.co/ml1D6pO8Ha
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!
Wang et al. (2026-06, Proc Biol Sci)
「ゲノム系統学により100年来の『ジュズヒゲムシ問題』が解決される:ジュズヒゲムシ類は多新翅類の中で最初に分岐した系統である」
Phylogenomics resolves the century-old ‘Zoraptera problem’: Zoraptera as the earliest diverging lineage of Polyneoptera