Signalling Network Construction for Modelling Plant Defence Response

Abstract

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for modelling other biological systems, given that an adequate vocabulary is provided.

Integrating semantic transcriptomic data analysis and knowledge extraction from biological literature

“…Among the correct triplets, biology experts recognised 113 as biologically sound. The achieved precision of Bio3graph in this use case is lower than expected [2] which can be attributed to the bigger and more general vocabulary.…”

“…In total, 100 rules composed of ontology terms (GO, KEGG) and interaction terms (Entrez) describing differentially expressed gene sets were provided. Using the complete set of rules, all differentially expressed genes and all their synonyms were used to compose the Bio3graph component vocabulary (the Supporting Information S4 from [2] was reused as the reactions vocabulary).…”

“…The approach presented in this paper fuses several processing components of two existing methodologies, SegMine [1] and Bio3graph [2] as well as extensive publicly available databases and search services into a novel data analysis workflow. Figure 1 depicts a schematic overview of the proposed approach.…”

“…Bio3graph enables the extraction of knowledge (biological relations) in the form of triplets (component1, reaction, component2). We refer the reader to the work of Miljkovic et al [2] for a detailed description and evaluation of Bio3graph.…”

“…Our goal is to discover relevant knowledge in biological literature, which will explain and support the results of the analysis of transcriptomic experimental data. We have merged the SegMine methodology [1] for the semantic analysis of microarray data and the Bio3graph system [2], which implements automated extraction of biological relations from the literature. Both systems represent state-of-the-art development in the corresponding research field.…”