In the next experiment, we aimed at characterizing the effects of data streamlining. In this process, we asked the following question. Would it be possible to identify patterns in data that might be responsible for the observed query performance deterioration as compared to the smaller scale (∼100K reactions) prototype database? In similar scenarios, it is typical to look for highly connected (high degree) nodes, and possibly filter them out from the database. The main problem with this approach, involves a non-discriminant treatment of reaction/substance nodes as long as their degree is above some pre-determined cutoff. Of course, the method to identify an adequate degree cutoff has its own challenges. While this direction can be indeed interesting in future analysis, as we will show, we found a more anchored, domain-knowledge driven approach to address this issue efficiently, in light of our performance goal.