This application requests support for the development of new software tools for analyzing metabolomics data. The proposed tools will analyze metabolomics data in the context of metabolic pathways obtained from the BioCyc database collection. A key motivation underlying our work is that it is advantageous to interpret metabolomics data in the context of existing metabolic knowledge, and that pathway databases that accurately map the metabolism of thousands of organisms are a logical source of pathway information. Known metabolic networks provide a valuable framework for interpreting metabolomics data because patterns in metabolomics data often result from patterns among the biochemical reactions that interconnect metabolites. However, the large size and high connectivity of organism metabolic networks, and the large number of path- ways within most organisms, present signi?cant cognitive barriers to this notion of using existing metabolic knowledge to analyze metabolomics data. A major motivation of this work is to lower these cognitive barri- ers by identifying subsets of the metabolic network for the user to focus their attention on. We will evaluate the following two proposed software tools by applying them to re-analyze existing metabolomics datasets previously generated by our group. Our ?rst aim is to develop software to compute minimal pathway covering sets for a set of metabolites, that is, a minimal number of metabolic pathways that contain the metabolites within a metabolomics dataset submitted by a user.
Our second aim i s to develop software to compute reaction spanning trees for metabolite sets to eluci- date mechanistic relationships among metabolites. As a companion to the preceding pathway-centric algorithm, this algorithm will consider the full metabolism of the organism as a graph, and will compute a minimal tree that spans the metabolites in a metabolomics dataset. Both the minimal pathway-covering sets and the reaction spanning trees will be displayed using existing pathway display algorithms in our Pathway Tools software. Fur- thermore, our new software will be integrated into Pathway Tools, which is a highly used software package in bioinformatics.
This project proposes to create new software tools for the analysis of metabolomics datasets. Although scientists can now measure changes in the activities of hundreds of small molecules in living systems, it can be very chal- lenging to elucidate the mechanisms responsible for those changes. Our proposed software will aid scientists in explaining patterns in metabolomics datasets by applying existing knowledge of metabolic pathways.
