Modeling the Evolution of Metabolic Networks

All biological phenomena finally rest upon the coordinated processes of metabolism. How the intricate network of catalyzed chemical reactions evolved to its present day complexity is unclear. Computational models can help to shed light on the origin and evolution of metabolic networks. These models have, however, demanding pre-requisites, since they must be capable of explaining the emergence of novel catalytic functions, at the lower level, as well as the origin of complex metabolic pathways and intricate structures on the level of the entire network. During chemical reactions molecules may change their quantitative physico-chemical properties, while atom types and mass is conserved. Furthermore, novel molecular species with hitherto unknown physico-chemical properties may arise, giving chemical processes an algebraic and thermodynamic structure. A formalization for both enzymes and chemical transformation is required, which is expressive enough to mimic the intricacy of a modern metabolic network, without restricting the possible chemistry to the known extant end results. I will briefly present our graph grammar based chemistry model followed by discussing results on (i) the evolution of metabolic networks and (ii) the identification of higher order transformation motifs, such as auto-catalysis, in these networks.