Cellular signaling systems play a vital role in maintaining homeostasis when a cell is exposed to different perturbations. Components of the systems are organized as hierarchical networks, and perturbing different components often leads to transcriptomic profiles that exhibit compositional statistical patterns. Mining such patterns to investigate how cellular signals are encoded is an important problem in systems biology, where artificial intelligence techniques can be of great assistance. Here, we investigated the capability of deep generative models (DGMs) to modeling signaling systems and learn representations of cellular states underlying transcriptomic responses to diverse perturbations. Specifically, we show that the variational autoencoder and the supervised vector-quantized variational autoencoder can accurately regenerate gene expression data in response to perturbagen treatments. The models can learn representations that reveal the relationships between different classes of perturbagens and enable mappings between drugs and their target genes. In summary, DGMs can adequately learn and depict how cellular signals are encoded. The resulting representations have broad applications, demonstrating the power of artificial intelligence in systems biology and precision medicine.

当细胞暴露于不同的扰动时，细胞信号系统在维持体内平衡方面起着至关重要的作用。系统的组件被组织为分层网络，并且扰乱不同的组件通常会导致表现出组成统计模式的转录组配置文件。挖掘此类模式以研究细胞信号的编码方式是系统生物学中的一个重要问题，其中人工智能技术可以提供很大帮助。在这里，我们研究了深度生成模型 (DGM) 对信号系统建模的能力，并学习了对各种扰动的转录组反应的细胞状态表示。具体来说，我们表明变分自编码器和有监督的矢量量化变分自编码器可以准确地重新生成基因表达数据以响应扰动处理。这些模型可以学习揭示不同类别扰动因子之间关系的表示，并能够在药物与其靶基因之间进行映射。总之，DGM 可以充分学习和描述细胞信号的编码方式。由此产生的表示具有广泛的应用，展示了人工智能在系统生物学和精准医学中的力量。DGM 可以充分学习和描述细胞信号的编码方式。由此产生的表示具有广泛的应用，展示了人工智能在系统生物学和精准医学中的力量。DGM 可以充分学习和描述细胞信号的编码方式。由此产生的表示具有广泛的应用，展示了人工智能在系统生物学和精准医学中的力量。