Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.
Department of Biotechnology and Systems Biology, National Institute of Biology, Večna pot 121, 1000 Ljubljana, Slovenia.
Plant Commun. 2024 Jun 10;5(6):100920. doi: 10.1016/j.xplc.2024.100920. Epub 2024 Apr 15.
Stress Knowledge Map (SKM; https://skm.nib.si) is a publicly available resource containing two complementary knowledge graphs that describe the current knowledge of biochemical, signaling, and regulatory molecular interactions in plants: a highly curated model of plant stress signaling (PSS; 543 reactions) and a large comprehensive knowledge network (488 390 interactions). Both were constructed by domain experts through systematic curation of diverse literature and database resources. SKM provides a single entry point for investigations of plant stress response and related growth trade-offs, as well as interactive explorations of current knowledge. PSS is also formulated as a qualitative and quantitative model for systems biology and thus represents a starting point for a plant digital twin. Here, we describe the features of SKM and show, through two case studies, how it can be used for complex analyses, including systematic hypothesis generation and design of validation experiments, or to gain new insights into experimental observations in plant biology.
应激知识图谱(SKM;https://skm.nib.si)是一个公开可用的资源,其中包含两个互补的知识图谱,描述了植物中生化、信号和调节分子相互作用的当前知识:一个经过高度策展的植物应激信号模型(PSS;543 个反应)和一个大型综合知识网络(488390 个相互作用)。这两个模型都是通过系统地策展各种文献和数据库资源,由领域专家构建的。SKM 为研究植物应激反应和相关生长权衡提供了一个单一的切入点,也为当前知识的交互式探索提供了一个单一的切入点。PSS 也被构造成系统生物学的定性和定量模型,因此代表了植物数字孪生的起点。在这里,我们描述了 SKM 的特点,并通过两个案例研究展示了如何使用它进行复杂的分析,包括系统地生成假设和设计验证实验,或深入了解植物生物学中的实验观察。