To enable “precision agriculture” and to determine the yield gap between present and potential crop production, we need to better understand the underlying biological processes of plant growth and crop yield. The RBnet project brings together a multidisciplinary research team in computer science and molecular genetics with an aim to map the topologies, its dynamic functioning and evolution of a central growth regulatory network connected to the plant Retinoblastoma Related Protein (RBR). RBR is a broadly utilized adaptor protein to dynamically regulate the assembly or disassembly of protein complexes on distinct batteries of genes and it provides a convergence point for signaling pathways for the regulation of cell proliferation, cell differentiation and metabolism. The major aims of the RBnet project are (i) to build an RBR protein-protein interaction network (ii) to computationally identify distinct RB complexes, (iii) perform experiments to verify some of these RBR complexes, (iv) identify RBR target genes and (v) study the dynamic behavior and functions of RBR complexes during leaf development. The research program and the associated structured training in computational biology will provide a strong basis for the fellow to become a leader in this filed and answer important questions, such as the systematic comparison of RB networks among organisms in plant and animal kingdoms, to enrich our knowledge on plant RB regulatory components in less studied organisms, such as crop plants, how the RB network has evolved and became modified for species specific outputs, compare growth-regulatory networks among crop plants, and identify ways to modify the network for optimum growth performance, how environmental factors, such as drought, limiting nutrients, pathogens impinge on growth through modulating network properties e.g. through RBR phosphorylation.