Automatic pathway reconstruction using text mining and visual analytics Pathway models are valuable resources that help us to understand the various mechanisms underpinning complex biological processes. Their curation is typically carried out through manual inspection of the scientific literature, a knowledge-intensive and laborious task. I will describe how text mining methods are used to automate model reconstruction by increasing the speed and reliability of discovery and extracting evidence from the literature. Information from the literature (events) can act as corroborative evidence of the validity of these reactions in a model or help to extend it by discovering new evidence. In addition, by contextualising the textual evidence (uncertainty, negation), we can provide additional confidence measures for linking and ranking information from the literature for model curation and ultimately experimental design. I will describe a visual text analytics system, LitPathExplorer which acts as the nexus between text mining methods and modellers by providing an interactive way to explore and analyse the statements linked with pathways.