title: 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.
authors.affiliation: Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia. dragana.miljkovic@ijs.si
authors.forename: Dragana
authors.initials: D
authors.lastname: Miljkovic
authors.forename: Tjaša
authors.initials: T
authors.lastname: Stare
authors.forename: Igor
authors.initials: I
authors.lastname: Mozetič
authors.forename: Vid
authors.initials: V
authors.lastname: Podpečan
authors.forename: Marko
authors.initials: M
authors.lastname: Petek
authors.forename: Kamil
authors.initials: K
authors.lastname: Witek
authors.forename: Marina
authors.initials: M
authors.lastname: Dermastia
authors.forename: Nada
authors.initials: N
authors.lastname: Lavrač
authors.forename: Kristina
authors.initials: K
authors.lastname: Gruden
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D054884:Host-Pathogen Interactions; D008954:Models, Biological; D010944:Plants / Q000276:immunology* / Q000378:metabolism*; D015203:Reproducibility of Results; D015398:Signal Transduction