Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization.
Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization.
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DOI：
10.1093/bioinformatics/btv660
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发表时间：
2016-03-15
期刊：
影响因子：
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通讯作者：
Wipat A
中科院分区：
文献类型：
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作者：
Misirli G;Cavaliere M;Waites W;Pocock M;Madsen C;Gilfellon O;Honorato-Zimmer R;Zuliani P;Danos V;Wipat A
动机：生物系统是复杂且具有挑战性的模型，因此模型重用是非常可取的。为了促进模型重用，模型应该包括关于模拟细节的信息和元数据形式的底层生物学。计算上可处理的元数据的可用性对于模型的有效自动解释和处理尤为重要。元数据通常表示为机器可读的注释，它增强了对模型信息的编程访问。基于规则的语言已经作为一种建模框架出现，以表示生物系统的复杂性。注释方法已广泛用于诸如SBML之类的基于反应的形式化。然而，基于规则的语言仍然缺乏丰富的注释框架来向模型的组件添加语义信息，例如机器可读的描述。结果：我们提出了一个注释框架和指南，用于注释基于规则的模型，用常用的Kappa和BioNetGen语言编码。我们将广泛采用的注释方法应用于基于规则的模型。我们最初提出了一种语法来存储机器可读的注释，并描述基于规则的建模实体（如代理和规则）及其注释之间的映射。然后，我们描述了一个本体来注释这些模型并捕获其中包含的信息，并使用示例演示注释这些模型。最后，我们提出了一个概念验证工具，用于从模型中提取注释，并且可以以统一的方式进行查询和分析。注释的统一表示可用于促进基于规则的模型的创建、分析、重用和可视化。尽管给出了示例，但使用特定的实现，所建议的技术通常可以应用于基于规则的模型。可用性和实现：基于规则的模型的注释本体可以在http://purl.org/rbm/rbmo上找到。krdf工具和相关的可执行示例可在http://purl.org/rbm/rbmo/krdf上获得。联系方式：anil.wipat@newcastle.ac.uk或vdanos@inf.ed.ac.uk
Motivation: Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. Results: We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. Availability and implementation: The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo. The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf. Contact: anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk