VCWorld: A Biological World Model for Virtual Cell Simulation
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866910034566316032 |
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| author | Wei, Zhijian Ma, Runze Wang, Zichen Li, Zhongmin Song, Shuotong Zheng, Shuangjia |
| author_facet | Wei, Zhijian Ma, Runze Wang, Zichen Li, Zhongmin Song, Shuotong Zheng, Shuangjia |
| contents | Virtual cell modeling aims to predict cellular responses to perturbations. Existing virtual cell models rely heavily on large-scale single-cell datasets, learning explicit mappings between gene expression and perturbations. Although recent models attempt to incorporate multi-source biological information, their generalization remains constrained by data quality, coverage, and batch effects. More critically, these models often function as black boxes, offering predictions without interpretability or consistency with biological principles, which undermines their credibility in scientific research. To address these challenges, we present VCWorld, a cell-level white-box simulator that integrates structured biological knowledge with the iterative reasoning capabilities of large language models to instantiate a biological world model. VCWorld operates in a data-efficient manner to reproduce perturbation-induced signaling cascades and generates interpretable, stepwise predictions alongside explicit mechanistic hypotheses. In drug perturbation benchmarks, VCWorld achieves state-of-the-art predictive performance, and the inferred mechanistic pathways are consistent with publicly available biological evidence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00306 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VCWorld: A Biological World Model for Virtual Cell Simulation Wei, Zhijian Ma, Runze Wang, Zichen Li, Zhongmin Song, Shuotong Zheng, Shuangjia Cell Behavior Artificial Intelligence Machine Learning Virtual cell modeling aims to predict cellular responses to perturbations. Existing virtual cell models rely heavily on large-scale single-cell datasets, learning explicit mappings between gene expression and perturbations. Although recent models attempt to incorporate multi-source biological information, their generalization remains constrained by data quality, coverage, and batch effects. More critically, these models often function as black boxes, offering predictions without interpretability or consistency with biological principles, which undermines their credibility in scientific research. To address these challenges, we present VCWorld, a cell-level white-box simulator that integrates structured biological knowledge with the iterative reasoning capabilities of large language models to instantiate a biological world model. VCWorld operates in a data-efficient manner to reproduce perturbation-induced signaling cascades and generates interpretable, stepwise predictions alongside explicit mechanistic hypotheses. In drug perturbation benchmarks, VCWorld achieves state-of-the-art predictive performance, and the inferred mechanistic pathways are consistent with publicly available biological evidence. |
| title | VCWorld: A Biological World Model for Virtual Cell Simulation |
| topic | Cell Behavior Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2512.00306 |