Project overview

Climate change and environmental stress it poses on plants, is one of the greatest challenges for humanity in the current century. Abiotic stresses (e.g. heat, cold, drought, salinity, light, nutrient deprivation) in combination with additional pathogen and pest pressure result in a global reduction of crop agricultural production, thus causing worldwide economic costs. At the same time, the field of plant physiology is maturing, with various methodologies enabling the collection of high-throughput data and insights into molecular mechanisms, thus progressing from analysis towards predictions.

Searching for motifs in large networks is most commonly performed on human or model organisms. In light of translational agronomy and discovery of traits that could be applied to agronomic improvement, transfer of knowledge to crop plants is required. The main objective of this research project is to elucidate new properties of stress signalling in plants, from perspectives of both a model organism (Arabidopsis thaliana) and a crop species (Solanum tuberosum). This will be achieved by an integrative multidisciplinary biology approach, merging areas of graph analysis, mathematical modelling and network inference techniques, supported with molecular biology experimentation in a plant biological setting.

We hypothesize that the combination of basic structural blocks placed within a specific time-frame leads to the phenotype and that studying these elementary components will inevitably increase our mechanistic understanding of plant processes. With that in mind, we have set the following specific objectives:

  1. To complement the existing Arabidopsis knowledge network with new data from public sources and to generate stress specific gene regulatory networks for both species.
  2. Extract network motifs from generated networks and enrich them with the use of ontologies and high-throughput datasets on transcriptional regulation and compare their existence in both species.
  3. Investigate the time aspect of selected motifs or smaller subsystems by mathematical modelling.
  4. Test our model predictions experimentally.