Modelling from gene to crop
The main objective of this part of WATBIO, led by Wageningen University, is to create a modelling cycle in which experimental results across the project serve as input and output to drive further experimentation. This connection of different levels of hierarchy, will result in predictive modelling of phenotypic responses. A further objective is to connect different WATBIO research activities by translating findings and integrating these in modular networks. The robustness of such networks will be simulated in response to abiotic stresses, particularly drought.
The research will establish a platform for the analysis and interpretation of data generated across the project. These include examinations of genotype and treatment effects on transcriptional programs, metabolic profiles and developmental processes. Emphasis will lie in connecting the different parts of WATBIO through integrated modelling.
Molecular level phenotypic data provide the basis for modelling of biological pathways and regulatory networks underlying drought tolerance. This includes transcriptomic, proteomic and metabolic data as well as hormone analyses. Most traits are being analysed at different stages of development, in different tissues and under different conditions thereby connecting the different levels of organisation. This will ultimately result in models for drought responses from physiological parameters to molecular levels which will guide future research.
The European gradient of climatic conditions incorporates additional environmental cues like temperature, photoperiod and light quality. Variations in watering regime, resulting in different levels of drought stress, will be studied. Analysis of data from the WATBIO experiments and predictive modeling should provide powerful insights into adaptive behaviour with respect to water supply and the impact of this on biomass yield and quality to develop datasets for G x E modeling activities with the aim of elucidating
both gene networks alongside trait dissection from this workpackage. Taken together this provides a powerful dataset that will enable key traits for drought tolerance, under field conditions to be elucidated through joined to underlying gene networks.This is a rich source of data that can be utilised for forward and reverse genetic approaches to elucidate genes linked to drought tolerance and improved breeding
efficiencies for these crops using a modelling approach.