Rapid advances in metagenomics and genome sequencing have led to the accumulation of vast amounts of empirical ecological data such as 16S, RNA-Seq etc. With the increase in ecological data production, the need for robust automated functional community analysis approaches rises, creating an information-analysis gap. An accumulating body of evidence now supports the reliability of metabolic analysis, such as metabolic networks, as a tool for processing genomic data into information describing the 'lifestyle' of microbial species and the network of interaction they form within such communities. The integration of multiple single species networks into a communal metabolic network representation allows for the investigation of the functional division between its participants, showing the metabolic hierarchy in the sampled environment. Here we apply the use of such metabolic network approaches for the functional division analysis of communities in the rhizosphere and bulk soil environments based on RNA Seq data that was collected for cucumber and wheat crops in Israel. Based on the enzyme content in each environment we applied computational approaches to characterize the unique metabolic environments. The computational survey points at the high diversity of glycans in the rhizosphere environment in comparison to soil, hence supporting the reliability of the in silico predictions. Through the application of the Expansion algorithm we predict community activity in each environment. By simulating community activity following the systematic deletion of community members, we aim at delineating its unique contribution to community metabolism. In particular we look for species' contribution to N-, S and glycan metabolism.
