Weixu Wang · 汪伟旭
Weixu Wang
Computational Biology · Single-Cell Dynamics

Weixu Wang汪伟旭

PhD Candidate, Theis Lab / Helmholtz Munich & Technical University of Munich
Email Google Scholar GitHub

I am a final-year PhD candidate at Helmholtz Munich and the Technical University of Munich, supervised by Prof. Fabian J. Theis. My research lies at the interface of machine learning, dynamical systems and single-cell genomics — I build interpretable deep-learning frameworks that read the language of gene regulation and predict how cells choose their fate.

My recent work, RegVelo (Cell, 2026), couples splicing kinetics with gene regulatory networks inside a NeuralODE backbone to enable mechanistic in silico perturbation — validated experimentally by CRISPR/Cas9 and single-cell Perturb-seq. Earlier, I co-developed hUSI (Nature Aging, 2025) for cellular senescence prediction and ICAnet / ENIGMA / NetID as part of my master's training with Prof. Ting Ni at Fudan University.

Seeking faculty positions starting 2026 — 2027
MIT
Featured Interview · MIT Technology Review China
Read the in-depth feature on RegVelo and the future of in silico cell modelling — published by 《麻省理工科技评论》
mittrchina.com · 2026
→
§ 01

Research Directions

01
Regulatory Dynamics of Single Cells
Coupling splicing kinetics with gene regulatory interactions inside a NeuralODE backbone. RegVelo enables actionable, interpretable predictions of cell fate transitions, with in silico perturbations validated by CRISPR and Perturb-seq.
02
Cellular Senescence at Scale
One-class machine learning that reads senescence from any transcriptome. hUSI generalises across tissues, conditions, and perturbations — a robust quantitative axis for ageing biology and therapeutic screening.
03
Disentangling Biology from Noise
ICAnet, ENIGMA, NetID — a toolkit of matrix completion and independent-component methods that separates biological signal from technical noise and recovers cell-type-specific expression and lineage-specific regulatory networks.
04
Foundation Models for Omics
Bridging large language models with single-cell biology. CellHermes (under review at Nature Methods) harmonises multimodal omics data through a unified language interface for omics understanding and reasoning.
§ 02

News & Updates

Jun 2026
MediaFull-page interview in Il Sole 24 Ore — Italy's national newspaper of record for business and finance (circulation ≈ 679,000) — featuring the Nature Aging hUSI study and its translational implications for senolytic therapy. PDF →
May 2026
MediaFeatured in 《麻省理工科技评论》 (MIT Technology Review China) — in-depth interview on RegVelo. Read →
May 2026
CellRegVelo published in Cell. Covered by Technology Networks, Phys.org, GEN, News-Medical, and the Stowers Institute press office.
Jul 2026
TalkInvited speaker, ECMTB 2026 (Graz) — Decoding Gene Regulatory Networks and Cellular Dynamics.
May 2026
TalkInvited speaker, Milan Longevity Summit 2026 — presenting the hUSI senescence framework.
2025
Nat. AginghUSI, a transcriptome-based universal senescence index, published in Nature Aging as co-corresponding author.
2025
TalkInvited talks at Imperial College London (Department of Mathematics) and GIBH, Guangzhou.
2024
Genome Biol.NetID for lineage-specific gene regulatory network inference accepted by Genome Biology.
2022
Joined Theis Lab at Helmholtz Munich / TUM as a PhD researcher.
§ 03

Selected Publications

* co-first  † co-corresponding
2026
Cell2026
RegVelo: gene-regulatory-informed dynamics of single cells
★ Featured in MIT Tech Review China · Press coverage
Wang, W.*, Hu, Z.*, Weiler, P.*, Mayes, S., Lange, M., Wang, J., Xue, Z., Sauka-Spengler, T.†, Theis, F. J.†
DOI Code Docs
2025
Nat. Aging2025
A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions
★ Featured in Il Sole 24 Ore (Italy, Jun 2026) · Milan Longevity Summit 2026
Wang, J., Zhou, X., Yu, P., Yao, J., Guo, P., Xu, Q., Zhao, Y., Wang, G., Li, Q., Zhu, X., Wei, G.†, Wang, W.†, Ni, T.†
DOI Code
bioRxiv2025
Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes
Under review at Nature Methods
Gao, Y.*, Wang, W.*, et al.
Code
2024
Genome Biol.2024
Scalable identification of lineage-specific gene regulatory networks from metacells with NetID
Wang, W., Wang, Y., Lyu, R., Grün, D.
DOI Code
2023
Nat. Commun.2023
Single-cell RNA-seq uncovers dynamic processes orchestrated by RNA-binding protein DDX43 in chromatin remodeling during spermiogenesis
Tan, H.*, Wang, W.*, Zhou, C., Wang, Y., Zhang, S., Yang, P., Guo, R., Chen, W., Zhang, J., Ye, L., Cui, Y.†, Ni, T.†, Zheng, K.†
DOI
Brief. Bioinform.2023
Approximate estimation of cell-type resolution transcriptome in bulk tissue through matrix completion (ENIGMA)
Wang, W.*, Zhou, X.*, Wang, J.*, et al.
DOICode
2021
NAR2021
ICAnet: Independent component analysis based gene co-expression network inference for single-cell clustering and batch integration
Wang, W., Tan, H., Sun, M., Han, Y., Chen, W., Qiu, S., Zheng, K., Wei, G., Ni, T.
DOICode
Selected
Science2024
The landscape of RNA binding proteins in mammalian spermatogenesis
Li, Y., Wang, Y., Tan, Y.-Q., …, Wang, W., …, Zheng, K.
Nat. Methods2025
Feature selection methods affect the performance of scRNA-seq data integration and querying
Zappia, L., Richter, S., Ramírez-Suástegui, C., Kfuri-Rubens, R., Vornholz, L., Wang, W., et al., Luecken, M. D., Theis, F. J.
Mol. Cancer2019
Pan-cancer analysis identifies telomerase-associated signatures and cancer subtypes
Luo, Z.*, Wang, W.*, Li, F., Songyang, Z., Feng, X., Xin, C., Dai, Z.†, Xiong, Y.†
Genome Res.2021
Cancer-associated dynamics and potential regulators of intronic polyadenylation revealed by IPAFinder using standard RNA-seq data
Zhao, Z., Xu, Q., Wei, R., Wang, W., Ding, D., Yang, Y., Yao, J., Zhang, L., Hu, Y.-Q., Wei, G., Ni, T.
NAR2021
Comprehensive characterization of somatic variants associated with intronic polyadenylation in human cancers
Zhao, Z., Xu, Q., Wei, R., Huang, L., Wang, W., Wei, G., Ni, T.
Commun. Biol.2023
Tox4 regulates transcriptional elongation and reinitiation during murine T cell development
Wang, T., Zhao, R., Zhi, J., Liu, Z., Wu, A., Yang, Z., Wang, W., Ni, T., Jing, L., Yu, M.
arXiv2025
A scalable gene network model of regulatory dynamics in single cells
Bertin, P., Viviano, J., Tejada-Lapuerta, A., Wang, W., Bauer, S., Theis, F. J., Bengio, Y.
arXiv2025
CellForge: Agentic Design of virtual cell models
Tang, X., Yu, Z., Chen, J., Cui, Y., Shao, D., Wang, W., Wu, F., Zhuang, Y., et al., Theis, F., Krishnaswamy, S., Gerstein, M.
bioRxiv2025
TGIF2 is a major regulator of neural stem cell fate and neurogenic priming
Li, Y., Krontira, A. C., Vierl, F., Richter, M. L., Wang, W., Merl-Pham, J., Theis, F. J., Hauck, S. M., Götz, M.
bioRxiv2024
Single-cell multi-omics, spatial transcriptomics and systematic perturbation decode circuitry of neural crest fate decisions
Hu, Z., Mayes, S., Wang, W., Santos-Pereira, J. M., Theis, F., Sauka-Spengler, T.
Research2025
Comprehensive cellular senescence evaluation to aid targeted therapies
Zhou, X., Zhu, X., Wang, W., Wang, J., Wen, H., Zhao, Y., Zhang, J., Xu, Q., Zhao, Z., Ni, T.
§ 04

Media Coverage

Press & interviews
Il Sole 24 Ore
«Ecco il termometro che misura come invecchiano le cellule» — Full-page interview on hUSI (Nature Aging) in Italy's leading national business daily (≈ 679k readers).
Salute 24 · 9 June 2026 · Print, p. 1 & 30
麻省理工科技评论
In-depth interview on RegVelo and in silico modelling of cell fate
MIT Technology Review China · 2026
Technology Networks
RegVelo AI Maps How Genes Drive Cell Fate Decisions
May 2026
Phys.org
New AI tool predicts how cells choose their future
May 2026
§ 05

Invited Talks

2026
Milan Longevity Summit 2026 A transcriptome-based universal senescence index for predicting cellular aging
Milan, IT
2026
ECMTB 2026 — 14th European Conf. on Math. & Theoretical Biology Decoding gene regulatory networks and cellular dynamics
Graz, AT
2025
Imperial College London — Department of Mathematics Decoding regulatory dynamics for cell fate transition modeling and prediction
London, UK
2025
Guangzhou Institutes of Biomedicine and Health (GIBH) Decoding regulatory dynamics for cell fate transition modeling and prediction
Guangzhou, CN
2023
ByteDance AI Lab / Research From cell configuration space to cell phase space
Shanghai, CN
§ 06

Awards & Honors

Outstanding Graduate of Shanghai Municipality — Ministry of Education, Shanghai
National Scholarship — Ministry of Education, China
§ 07

Experience

Helmholtz Munich & TUMMunich, DE
PhD Researcher · Theis Lab Computational biology & machine learning. Lead developer of RegVelo (Cell, 2026). Supervisor: Prof. Fabian J. Theis.
Oct 2022 – Present
Fudan UniversityShanghai, CN
M.Sc. Researcher · Bioinformatics Co-developed hUSI (Nature Aging), ICAnet (NAR), and ENIGMA. Supervisor: Prof. Ting Ni.
Sep 2019 – Jun 2022
§ 08

Academic Service

Conference Reviewer
RECOMB — Research in Computational Molecular Biology
Journal Reviewer
Nature Communications · Genome Biology · Nucleic Acids Research · Briefings in Bioinformatics
Open Source
Lead developer of RegVelo, ICAnet, ENIGMA, NetID — available on GitHub with full documentation.