This work focuses on automated incremental development of biological networks. The
Bio3graph approach to information extraction from biological literature is extended with new features
which allow for periodical updates of network structures using newly published scientific literature. The incremental
approach is demonstrated on two use cases. First, a simple plant defence network with 37 components and 49 relations
created manually by merging three existing structural models is extended in two incremental steps, yielding the final
model with 183 relations. Second, a complex published network of defence response in Arabidopsis thaliana, containing
175 nodes and 524 relations, is incrementally updated with information extracted from recently published articles
resulting in an enhanced network with 628 links. The results show that using the demonstrated incremental approach it is
possible to automatically recognise new knowledge about the selected biological relations published in recent literature.
The newly implemented Bio3graph extension offers an effective way of merging and visually representing the initial
networks and the networks generated from texts thus enabling fast discovery of relations which can potentially enhance
the existing models.