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继2008年人类首次化学合成生殖支原体基因组后,合成生物学领域今日迎来了一项被誉为“ChatGPT时刻”的范式突破。由Arc研究所、斯坦福大学、NVIDIA及加州大学伯克利分校等机构联合研发的生物基础模型——Evo2正式对外公布。该模型基于9万亿个DNA碱基对训练而成,不仅能够高精度预测基因突变的致病性,更实现了从头设计长达百万碱基对的复杂基因组序列,标志着生命代码的操纵从“微调编辑”正式跨入“生成式编程”时代。2026年3月4日,相关研究成果发表于《Nature》。

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(图片来源:斯坦福大学官网)

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超长记忆让AI开始改写小鼠的DNA
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科技导报 | 科技新闻 2026,44(5): 3-3
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超长记忆让AI开始改写小鼠的DNA
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    出版时间: 2026-03-13
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    继2008年人类首次化学合成生殖支原体基因组后,合成生物学领域今日迎来了一项被誉为“ChatGPT时刻”的范式突破。由Arc研究所、斯坦福大学、NVIDIA及加州大学伯克利分校等机构联合研发的生物基础模型——Evo2正式对外公布。该模型基于9万亿个DNA碱基对训练而成,不仅能够高精度预测基因突变的致病性,更实现了从头设计长达百万碱基对的复杂基因组序列,标志着生命代码的操纵从“微调编辑”正式跨入“生成式编程”时代。2026年3月4日,相关研究成果发表于《Nature》。

    . 超长记忆让AI开始改写小鼠的DNA. 科技导报, 2026 , 44 (5) : 3 -3 .
    . Science & Technology Review, 2026 , 44 (5) : 3 -3 .
    继2008年人类首次化学合成生殖支原体基因组后,合成生物学领域今日迎来了一项被誉为“ChatGPT时刻”的范式突破。由Arc研究所、斯坦福大学、NVIDIA及加州大学伯克利分校等机构联合研发的生物基础模型——Evo2正式对外公布。该模型基于9万亿个DNA碱基对训练而成,不仅能够高精度预测基因突变的致病性,更实现了从头设计长达百万碱基对的复杂基因组序列,标志着生命代码的操纵从“微调编辑”正式跨入“生成式编程”时代。2026年3月4日,相关研究成果发表于《Nature》。
    人类基因组的建模一直受困于序列极长且包含大量长程依赖的特征。传统的Transformer架构在处理长序列时计算量呈平方级增长,难以完整“阅读”长达数百万碱基的遗传信息。Evo2凭借创新的StripedHyena2混合架构(结合了卷积和注意力机制),成功将“工作记忆”(上下文窗口)扩展至100万碱基对,并保持单核苷酸级的分辨率。这意味着AI不再只能分析基因片段,而是具备了理解整本“基因组小说”的能力,其处理吞吐量是传统模型的3倍以上。
    该模型在一个庞大、经过科学策划的数据集OpenGenome2上训练,该数据集包含约8.8万亿个来自细菌、古菌、真核生物和噬菌体的核苷酸,同时出于生物安全考虑,有意排除感染真核宿主的病毒。
    Evo2的性能主要在2个关键方向上进行评估:一是预测,即判断特定DNA突变或其他遗传变异是否会导致疾病或功能丧失;二是生成,即在指导条件下进行合成DNA序列的从头设计(denovodesign)。在针对BRCA1和BRCA2等关键致病基因的测试中,模型在无需任何任务特异性微调的情况下,识别致病突变的准确率超过90%。更具突破性的是,研究团队引导Evo2设计了特定的DNA序列,并将包含“EVO2”、“ARC”和“LO”(这是互联网传输的第一条信息)字样的摩斯密码信息成功编码进小鼠细胞的染色质拓扑结构中,并通过评价基因转录性的实验验证。
    研究团队利用稀疏自编码器(SAEs)对Evo2的内部表示进行机制可解释性分析,从而识别出与具体生物学功能相关的潜在特征,例如外显子-内含子边界、噬菌体相关基因组区域以及与遗传突变模式相关的编码特征。这表明Evo2在训练过程中自动学习到基因组结构规律,而不仅仅是统计模式。
    尽管Evo2生成的线粒体和细菌基因组在计算机预测中显示70% 的基因结构“看起来很合理”,但合成生物学家依然保持警惕。瓦赫宁根大学教授Nico-Claassens指出:“生命设计不能只完成70%。只要有一个关键代谢位点存在语法错误,整个合成生命就无法正常运行”。此外,德克萨斯大学奥斯汀分校的独立评估显示,Evo2生成的长序列在处理高重复序列(如异染色质区域)时容易发生“低复杂度崩溃”,表现出明显的同质化偏差。
    Evo2标志着生物学研究范式的一次重要转变:从以往针对单一生物组分的分析,迈向对整个基因组复杂性的整体建模。凭借超长上下文窗口及机制层面的关键创新,该模型能够识别并解析普遍性的进化规律,并实现从单细胞生物到人类等复杂生命系统的跨尺度泛化。
    (综合:《中国科学报》《Nature》、斯坦福大学官网)

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    2种不同金属材料的力学参数
    科
    Family
    属数
    Number of
    genus
    种数
    Number of
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    占总种数比例
    Percentage of
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    属
    Genus
    种数
    Number of
    species
    占总种数比例
    Percentage of total
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    鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
    小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
    多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
    红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
    小菇属 Mycena 11 5.26
    光柄菇属 Pluteus 5 2.39
    红菇属 Russula 17 8.13
    栓菌属 Trametes 5 2.39
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