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如何利用人工智能构建虚拟细胞:优先事项与机遇

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.

作者信息

Bunne Charlotte, Roohani Yusuf, Rosen Yanay, Gupta Ankit, Zhang Xikun, Roed Marcel, Alexandrov Theo, AlQuraishi Mohammed, Brennan Patricia, Burkhardt Daniel B, Califano Andrea, Cool Jonah, Dernburg Abby F, Ewing Kirsty, Fox Emily B, Haury Matthias, Herr Amy E, Horvitz Eric, Hsu Patrick D, Jain Viren, Johnson Gregory R, Kalil Thomas, Kelley David R, Kelley Shana O, Kreshuk Anna, Mitchison Tim, Otte Stephani, Shendure Jay, Sofroniew Nicholas J, Theis Fabian, Theodoris Christina V, Upadhyayula Srigokul, Valer Marc, Wang Bo, Xing Eric, Yeung-Levy Serena, Zitnik Marinka, Karaletsos Theofanis, Regev Aviv, Lundberg Emma, Leskovec Jure, Quake Stephen R

机构信息

Department of Computer Science, Stanford University, Stanford, CA, USA.

Genentech, South San Francisco, CA, USA.

出版信息

ArXiv. 2024 Oct 14:arXiv:2409.11654v2.

Abstract

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable experiments to predict and understand their behavior using Virtual Instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.

摘要

细胞可以说是生命最基本的单位,对于理解生物学至关重要。准确地对细胞进行建模,对于这种理解以及确定疾病的根本原因都很重要。人工智能(AI)的最新进展,加上生成大规模实验数据的能力,为细胞建模带来了新的机遇。在此,我们提出一种设想,即利用AI的进展构建虚拟细胞,即在不同条件下对细胞和细胞系统进行高保真模拟,这些模拟直接从跨测量和尺度的生物学数据中学习得到。我们讨论了此类AI虚拟细胞所需具备的能力,包括生成跨尺度生物实体的通用表示,以及利用虚拟仪器促进可解释的实验,以预测和理解它们的行为。我们进一步探讨了实现这一设想所面临的挑战、机遇和要求,包括数据需求、评估策略,以及确保生物学准确性和广泛实用性的社区标准与参与度。我们设想在未来,AI虚拟细胞将有助于识别新的药物靶点,预测细胞对干扰的反应,以及拓展假设探索。通过包括学术界、慈善机构、生物制药和AI行业在内的生物医学生态系统中的开放科学合作,对细胞机制和相互作用进行全面的预测性理解已触手可及。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eb4f/11487598/5bcbf27d29e0/nihpp-2409.11654v2-f0001.jpg

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