Kaltdorf Martin, Dandekar Thomas, Naseem Muhammad
Functional Genomics & Systems Biology Group, Department of Bioinformatics, Biocenter, University of Würzburg, 97074, Wuerzburg, Germany.
Deptartment of Bioinformatics, Biocenter University of Würzburg Am Hubland Würzburg Germany, Julius-Maximilians-Universität Würzburg, Würzburg, Germany.
Methods Mol Biol. 2017;1569:83-92. doi: 10.1007/978-1-4939-6831-2_6.
In order to increase our understanding of biological dependencies in plant immune signaling pathways, the known interactions involved in plant immune networks are modeled. This allows computational analysis to predict the functions of growth related hormones in plant-pathogen interaction. The SQUAD (Standardized Qualitative Dynamical Systems) algorithm first determines stable system states in the network and then use them to compute continuous dynamical system states. Our reconstructed Boolean model encompassing hormone immune networks of Arabidopsis thaliana (Arabidopsis) and pathogenicity factors injected by model pathogen Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) can be exploited to determine the impact of growth hormones in plant immunity. We describe a detailed working protocol how to use the modified SQUAD-package by exemplifying the contrasting effects of auxin and cytokinins in shaping plant-pathogen interaction.
为了增进我们对植物免疫信号通路中生物学依赖性的理解,对植物免疫网络中已知的相互作用进行了建模。这使得通过计算分析来预测生长相关激素在植物与病原体相互作用中的功能成为可能。SQUAD(标准化定性动力学系统)算法首先确定网络中的稳定系统状态,然后利用这些状态来计算连续动力学系统状态。我们重建的布尔模型涵盖了拟南芥的激素免疫网络以及模式病原体丁香假单胞菌番茄致病变种DC3000(Pst DC3000)注入的致病因子,可用于确定生长激素对植物免疫的影响。我们通过举例说明生长素和细胞分裂素在塑造植物与病原体相互作用中的对比效应,描述了一个详细的操作流程,介绍如何使用修改后的SQUAD软件包。