Automated extraction of meaningful pathways from quantitative proteomics data

Technological developments in the life sciences have resulted in an ever-accelerating pace of data production. Systems Biology tries to shed light upon these data by building complex models describing the interactions between biological components. However, extracting information from this morass of data requires the use of sophisticated computational techniques. Here, we propose a method suitable to integrate data drawn from quantitative proteomics into a metabolic scaffold and identify the metabolic pathways which are collectively up-regulated or down-regulated. The availability of such a tool is highly desirable as the extracted information could then be taken as a starting point for in-depth analyses, in particular in fields like Synthetic Biology, where datasets need be characterized routinely.