{"id":5189,"date":"2025-03-11T16:20:18","date_gmt":"2025-03-11T08:20:18","guid":{"rendered":"https:\/\/guomics.com\/?page_id=5189"},"modified":"2025-03-26T14:09:46","modified_gmt":"2025-03-26T06:09:46","slug":"way","status":"publish","type":"page","link":"https:\/\/guomics.com\/zh\/way\/","title":{"rendered":"The WAY project: Westlake AI Virtual Cell \u2013 Yeast"},"content":{"rendered":"

\u5bf9\u4e0d\u8d77\uff0c\u6b64\u5185\u5bb9\u53ea\u9002\u7528\u4e8eEN<\/a>\u3002<\/p>

Artificial Intelligence Virtual Cells (AIVCs) represent a paradigm shift in life science, offering unprecedented potential for in silico experimentation in biomedical research. However, how to construct robust AIVCs remains unclear. Here, we introduce the Westlake AI Virtual Cell – Yeast (WAY) project, a pioneering initiative to develop a comprehensive AIVC for Saccharomyces cerevisiae. Our approach is built on three essential data pillars: a priori knowledge, static architecture, and dynamic states. We emphasize the critical role of perturbation proteomics in capturing cellular dynamics, drawing from our previous success in developing neural network ODE models for predicting drug sensitivity and combination synergy in cancer cell lines. The WAY project aims to conduct large-scale, multi-omics, and multi-modal data collection through extensive perturbation experiments on yeast. By integrating diverse data types, including literature knowledge, multi-omics, structures and imaging, among others, we will employ a closed-loop active learning system that combines AI algorithms with automated experimentation. This iterative approach will eventually develop AIVCs that could evolve autonomously, continuously refining their predictive capabilities. The WAY project represents the first step towards creating a fully functional, self-evolving virtual yeast cell, with implications for advancing our understanding of cellular biology, drug discovery, and synthetic biology. We invite contributions from the scientific community to the WAY project.<\/p>\n

 <\/p>\n

Key references<\/strong><\/p>\n

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence<\/strong><\/a><\/p>\n

Yingying Sun, Jun A, Zhiwei Liu, Rui Sun, Liujia Qian, Samuel H. Payne, Wout Bittremieux, Markus Ralser, Chen Li, Yi Chen, Zhen Dong, Yasset Perez-Riverol, Asif Khan, Chris Sander, Ruedi Aebersold, Juan Antonio Vizca\u00edno, Jonathan R Krieger, Jianhua Yao, Han Wen, Linfeng Zhang, Yunping Zhu, Yue Xuan, Benjamin Boyang Sun, Liang Qiao, Henning Hermjakob, Haixu Tang, Huanhuan Gao, Yamin Deng, Qing Zhong, Cheng Chang, Nuno Bandeira, Ming Li, Weinan E, Siqi Sun, Yuedong Yang, Gilbert S. Omenn, Yue Zhang, Ping Xu, Yan Fu, Xiaowen Liu, Christopher M. Overall, Yu Wang, Eric W. Deutsch, Luonan Chen, J\u00fcrgen Cox, Vadim Demichev, Fuchu He, Jiaxing Huang, Huilin Jin, Chao Liu, Nan Li, Zhongzhi Luan, Jiangning Song, Kaicheng Yu, Wanggen Wan, Tai Wang, Kang Zhang, Le Zhang, Peter A. Bell, Matthias Mann*, Bing Zhang*, Tiannan Guo*.<\/p>\n

arXiv preprint. 21 Feb 2025. https:\/\/doi.org\/10.48550\/arXiv.2502.15867<\/p>\n

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Grow AI Virtual Cells: Three Data Pillars and Closed-Loop Learning<\/strong><\/a><\/p>\n

Liujia Qian, Zhen Dong, Tiannan Guo*.<\/p>\n

Cell Research<\/strong><\/em>. 2024 March 25. https:\/\/doi.org\/10.1038\/s41422-025-01101-y<\/p>\n

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A perturbation proteomics-based foundation model for virtual cell construction<\/strong><\/a><\/p>\n

Rui Sun#, Liujia Qian#, Yongge Li#, Honghan Cheng, Zhangzhi Xue, Xuedong Zhang, Lingling Tan, Yuecheng Zhan, Wenbin Hu, Qi Xiao, Zhiwei Liu, Guangmei Zhang, Weinan E, Peijie Zhou, Han Wen*, Yi Judy Zhu*, Tiannan Guo*.<\/p>\n

bioRxiv preprint. 10 Feb, 2025. doi: https:\/\/doi.org\/10.1101\/2025.02.07.637070.<\/p>\n

 <\/p>\n

Automating the practice of science: Opportunities, challenges, and implications<\/strong><\/a><\/p>\n

Musslick S*, Bartlett LK, Chandramouli SH, Dubova M, Gobet F, Griffiths TL, Hullman J, King RD, Kutz JN, Lucas CG, Mahesh S, Pestilli F, Sloman SJ, Holmes WR.<\/p>\n

Proc Natl Acad Sci USA<\/strong><\/em>. 4 Feb, 2025; 122(5):e2401238121. doi: 10.1073\/pnas.2401238121<\/p>\n

 <\/p>\n

How to build the virtual cell with artificial intelligence: Priorities and opportunities<\/strong><\/a><\/p>\n

Bunne C#, Roohani Y#, Rosen Y#, Gupta A, Zhang X, Roed M, Alexandrov T, AlQuraishi M, Brennan P, Burkhardt DB, Califano A, Cool J, Dernburg AF, Ewing K, Fox EB, Haury M, Herr AE, Horvitz E, Hsu PD, Jain V, Johnson GR, Kalil T, Kelley DR, Kelley SO, Kreshuk A, Mitchison T, Otte S, Shendure J, Sofroniew NJ, Theis F, Theodoris CV, Upadhyayula S, Valer M, Wang B, Xing E, Yeung-Levy S, Zitnik M, Karaletsos T*, Regev A*, Lundberg E*, Leskovec J*, Quake SR*.<\/p>\n

Cell<\/strong><\/em>. 12 Dec, 2024;187(25):7045-7063. doi: 10.1016\/j.cell.2024.11.015.<\/p>\n

 <\/p>\n

Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control<\/a><\/strong><\/p>\n

Perrino G#, Napolitano S#, Galdi F, La Regina A, Fiore D, Giuliano T, di Bernardo M, di Bernardo D*.<\/p>\n

Nat Commun<\/strong><\/em>. 27 Apr, 2021; 12(1):2452. doi: 10.1038\/s41467-021-22689-w.<\/p>\n

 <\/p>\n

Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast<\/strong><\/a><\/p>\n

Coutant A#, Roper K#, Trejo-Banos D#, Bouthinon D, Carpenter M, Grzebyta J, Santini G, Soldano H, Elati M, Ramon J, Rouveirol C, Soldatova LN, King RD*.<\/p>\n

Proc Natl Acad Sci USA<\/strong><\/em>. 3 Sep, 2019; 116(36):18142-18147. doi: 10.1073\/pnas.1900548116.<\/p>\n

 <\/p>","protected":false},"excerpt":{"rendered":"

\u5bf9\u4e0d\u8d77\uff0c\u6b64\u5185\u5bb9\u53ea\u9002\u7528\u4e8eEN\u3002Artificial Intelligence Virtual Cells (AI […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-5189","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/guomics.com\/zh\/wp-json\/wp\/v2\/pages\/5189","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/guomics.com\/zh\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/guomics.com\/zh\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/guomics.com\/zh\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/guomics.com\/zh\/wp-json\/wp\/v2\/comments?post=5189"}],"version-history":[{"count":17,"href":"https:\/\/guomics.com\/zh\/wp-json\/wp\/v2\/pages\/5189\/revisions"}],"predecessor-version":[{"id":5241,"href":"https:\/\/guomics.com\/zh\/wp-json\/wp\/v2\/pages\/5189\/revisions\/5241"}],"wp:attachment":[{"href":"https:\/\/guomics.com\/zh\/wp-json\/wp\/v2\/media?parent=5189"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}