Bioinformatics. 2017 Apr 1;33(7):1040-1048. doi: 10.1093/bioinformatics/btw747.
Literature on complex diseases is abundant but not always quantitative. Many molecular pathways are qualitatively well described but this information cannot be used in traditional quantitative mathematical models employed in drug development. Tools for analysis of discrete networks are useful to capture the available information in the literature but have not been efficiently integrated by the pharmaceutical industry. We propose an expansion of the usual analysis of discrete networks that facilitates the identification/validation of therapeutic targets.
In this article, we propose a methodology to perform Boolean modeling of Systems Biology/Pharmacology networks by using SPIDDOR (Systems Pharmacology for effIcient Drug Development On R) R package. The resulting models can be used to analyze the dynamics of signaling networks associated to diseases to predict the pathogenesis mechanisms and identify potential therapeutic targets.
The source code is available at https://github.com/SPIDDOR/SPIDDOR .
itzirurzun@alumni.unav.es , itroconiz@unav.es.
Supplementary data are available at Bioinformatics online.
关于复杂疾病的文献很多，但并不总是定量的。许多分子途径在定性上描述得很好，但这些信息不能用于药物开发中传统的定量数学模型。离散网络分析工具可用于捕获文献中的可用信息，但尚未被制药行业有效地整合。我们提出了一种扩展离散网络常规分析的方法，以方便鉴定/验证治疗靶点。
在本文中，我们提出了一种使用 SPIDDOR（用于在 R 上高效进行药物开发的系统药理学）R 包对系统生物学/药理学网络进行布尔建模的方法。由此产生的模型可用于分析与疾病相关的信号转导网络的动态，以预测发病机制并确定潜在的治疗靶点。
源代码可在 https://github.com/SPIDDOR/SPIDDOR 获得。
itzirurzun@alumni.unav.es，itroconiz@unav.es。
补充数据可在生物信息学在线获得。
