With our database, we aim to find combinations of ingredients that occur repeatedly and are specifically used to treat infectious diseases. To achieve this, we are employing some common tools of data science, such as network analysis, a mathematical method to examine the relationships between entries. Our team will then examine how these patterns may help us to use medieval texts as inspiration for lab tests of candidate “ancientbiotic” recipes.

In March, we tested a small portion of the database to ensure that the method we developed was appropriate for this data set. At present, the database contains only the 360 recipes indicated with Rx. Now that the proof-of-concept stage is complete, I will expand the database to contain other ingredients which are clearly in recipe format, but may not be marked with Rx.

We are specifically interested in recipes associated with recognizable signs of infection. With Bald’s eyesalve, the combination of ingredients proved to be crucial. By examining the strength of ingredient relationships, we hope to find out whether medieval medical recipes are driven by certain combinations of antimicrobial ingredients.

The database could direct us to new recipes to test in the lab in our search for novel antibiotics, as well as inform new research into the antimicrobial agents contained in these ingredients on the molecular level. It could also deepen our understanding of how medieval practitioners “designed” recipes. Our research is in the beginning stages, but it holds exciting potential for the future.

Erin Connelly, CLIR-Mellon Fellow for Data Curation in Medieval Studies, University of Pennsylvania

This article was originally published on The Conversation. Read the original article.