Background Climate change, especially climatic variability, poses critical challenges to European agriculture causing drought and high temperature stress to crops, resulting in productivity and yield loss. Camelina sativa is a native traditional European oilseed crop which has regained some attention thanks to its adaptability, yield stability and high performance in variable environments. The UNTWIST project will unravel the stress response mechanisms of Camelina in a multidisciplinary scientific project. UNTWIST generates an unprecedented dataset, coming from a core collection of 54 Camelina sativa lines growing under different stresses and locations across Europe in greenhouses and fields. Results The top-down modelling was used to predict phenotypic field traits from metabolic data. This involved creating a predictive model based on machine learning by combining phenotypic field data with metabolic data. The metabolic data was obtained from targeted and untargeted metabolomics of early stage leaves of core collection Camelina lines grown in greenhouse under control condition and water or thermal stress. We were able to predict Thousand Kernel Weight (TKW) and fatty acid content across various stresses. Genomic predictions were performed on the same data, resulting in the successful prediction of TKW as well as other morphological and phenotypic variables. A comparison between the metabolomics approach and the genetic approach was conducted, revealing differences in terms of cost, time, and processing difficulty. The bottom-up approach focus on the growth and the development of the fruit (seed and silique) by the reconstruction of four compartmentalized genome scale metabolic networks of four focus lines of Camelina that have been identified as representing the diversity of responses to stress, based on their genomic data. We have thus generated four first drafts of metabolic networks from different Camelina lines. The metabolic data used for the top-down modelling is currently used to calculate input and output fluxes of each model in order to constrain networks to give insights into the mechanism involved in drought and heat tolerance. A first draft flux map was obtained. These networks will be later refined with transcriptomics, proteomics and DNA methylation data from the four focus lines. Conclusions. The UNTWIST project's comprehensive analysis of Camelina sativa's stress response mechanisms will advance our understanding of crop resilience in European agriculture, ultimately contributing to the development of more sustainable and climate-resilient farming practices.