Elhanan Borenstein (Stanford University & Santa Fe Institute)
Thursday, 8.1.2009, 13:30
The topology of metabolic networks may provide important insights not only into the metabolic capacity of species, but also into the habitats in which they evolved. In this talk I will present several analyses of metabolic networks and show how various ecological insights can be obtained from genomic-based data.
I will first introduce various environmental and genetic factors that affect the structure of metabolic networks. I will then present the first large-scale computational reconstruction of metabolic growth environments, analyzing the metabolic networks of hundreds of species and using a graph-theory based algorithm to identify for each species a set of seed compounds that must be exogenously acquired. Such seed sets form ecological "interfaces" between metabolic networks and their surroundings, approximating the effective biochemical environment of each species. Computational reconstruction of metabolic networks of ancestral species and phylogenetic analysis of the seed sets reveal the complex dynamics governing gain and loss of biosynthetic capacity across the phylogenetic tree.
I will further present an extension of this framework, accounting for interactions between species, by introducing a pair-wise, topology-based measure of biosynthetic support, which reflects the extent to which the nutritional requirements of one species could be satisfied by the biosynthetic capacity of another. I will show that this measure is aligned with host-parasite interactions and facilitates successful prediction of such interactions on a large-scale.
Finally, I will discuss the application of this approach to the analysis of microbial communities and metagenomic data of the human microbiota and outline future research directions; The "reverse ecology" approach demonstrated in these analyses lays the foundations for further studying the complex web of interactions characterizing various ecosystems and the evolutionary interplay between organisms and their habitats on a large scale.