The University of Potsdam

Dr Zoran Nikoloski from The University of Potsdam will lead the data management, modelling and biostatistics for the project and will contribute metabolic and statistical modelling approach that will help rank parameters (i.e. kinetic properties and abundance of enzymes) and traits whose fine-tuning will lead to projected improvements of photosynthetic efficiency. Emphasis will be placed on the following three metabolic modelling tasks: (i) integration of genotype-specific time-resolved metabolomics data on Calvin-Benson-cycle (CBC) intermediates into kinetic models of photosynthesis to identify bottlenecks together with parameters which can mitigate their negative effects, (ii) analysis of the parameters that can reproduce the observed natural variance in photosynthetic responses to changed irradiance together with the parameters specific to the low-to-high and high-to-low irradiance in different species, and (iii) prediction of the direction of change in the identified parameters to fine-tune photosynthesis. In addition, Nikoloski’s group, will be involved in the task of analysing time-series data generated by the different high-throughput phenotyping platforms and will contribute approaches to integrate the resulting heterogeneous data (including genomics data). These tasks will result in predictive models at different scales, from the cell to the leaf level and the canopy