Stress Knowledge Map: A knowledge graph resource for systems biology analysis of plant stress responses

被引:6
|
作者
Bleker, Carissa [1 ]
Ramsak, Ziva [1 ]
Bittner, Andras [2 ]
Podpecan, Vid [3 ]
Zagorscak, Maja [1 ]
Wurzinger, Bernhard [4 ]
Baebler, Spela [1 ]
Petek, Marko [1 ]
Kriznik, Maja [1 ]
van Dieren, Annelotte [2 ]
Gruber, Juliane [4 ]
Afjehi-Sadat, Leila [5 ]
Weckwerth, Wolfram [4 ]
Zupanic, Anze [1 ]
Teige, Markus [4 ]
Vothknecht, Ute C. [2 ]
Gruden, Kristina [1 ]
机构
[1] Natl Inst Biol, Dept Biotechnol & Syst Biol, Vna Pot 121, Ljubljana 1000, Slovenia
[2] Univ Bonn, Inst Cellular & Mol Bot, Plant Cell Biol, Kirschallee 1, D-53115 Bonn, Germany
[3] Joef Stefan Inst, Dept Knowledge Technol, Jamova Cesta 39, Ljubljana 1000, Slovenia
[4] Univ Vienna, Dept Funct & Evolutionary Ecol, Djerassipl 1, A-1030 Vienna, Austria
[5] Univ Vienna, Core Facil Shared Serv, Mass Spectrometry Unit, Djerassipl 1, A-1030 Vienna, Austria
关键词
knowledge graph; plant stress responses; plant signaling; systems biology; plant digital twin; ARABIDOPSIS-THALIANA; COMPUTATIONAL PLATFORM; GENE; VISUALIZATION; GENERATION; DATABASE; PACKAGE; SIGNALS;
D O I
10.1016/j.xplc.2024.100920
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
收藏
页数:15
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