Biganzoli Elia Mario, Bollati Valentina
Unit of Medical Statistics, Bioinformatics and Epidemiology, Department of Biomedical and Clinical Sciences (DIBIC), Università degli Studi di Milano, 20157, Milan, Italy.
INES (Institute of Epigenetics for Smiles), Università degli Studi di Milano, 20157, Milan, Italy.
Environ Epigenet. 2025 Jul 1;11(1):dvaf022. doi: 10.1093/eep/dvaf022. eCollection 2025.
Traditionally viewed as a set of switches regulating gene expression, epigenetic mechanisms may also operate as an information-processing system with symbolic and subsymbolic features. In this framework, gene-specific DNA methylation and other localized epigenetic marks act as symbolic 'on/off' signals, while repetitive and noncoding DNA elements form a substrate for probabilistic, distributed responses to environmental stimuli. This hybrid perspective parallels machine-learning approaches, where symbolic representations are combined with subsymbolic methods (e.g. neural networks) to achieve robust learning and adaptation. Here, we propose that epigenetic regulation integrates these two dimensions (i.e. symbolic control and subsymbolic redundancy) to enable cells to adapt to complex environmental challenges, maintain heritable memory of past exposures, and evolve. In this manuscript, we introduce the concept of epigenetic intelligence, clarifying the synergy between discrete, 'symbolic' epigenetic switches (e.g. gene-specific DNA methylation) and the more 'subsymbolic', distributed features of the genome (e.g. repetitive elements methylation). This approach appears to be novel, as existing literature has not explicitly framed epigenetic regulation within a neuro-symbolic artificial intelligence perspective.
传统上，表观遗传机制被视为一组调节基因表达的开关，它也可能作为一个具有符号和亚符号特征的信息处理系统运行。在这个框架中，基因特异性DNA甲基化和其他局部表观遗传标记充当符号化的“开/关”信号，而重复和非编码DNA元件则形成对环境刺激进行概率性、分布式响应的底物。这种混合观点类似于机器学习方法，即符号表示与亚符号方法（如神经网络）相结合以实现强大的学习和适应能力。在此，我们提出表观遗传调控整合了这两个维度（即符号控制和亚符号冗余），使细胞能够适应复杂的环境挑战，维持对过去暴露的可遗传记忆并实现进化。在本手稿中，我们引入了表观遗传智能的概念，阐明了离散的“符号化”表观遗传开关（如基因特异性DNA甲基化）与基因组更“亚符号化”的分布式特征（如重复元件甲基化）之间的协同作用。这种方法似乎是新颖的，因为现有文献尚未在神经符号人工智能的视角下明确阐述表观遗传调控。
