This work (pdf) was done during my postdoc at the BarabasiLab. We investigated the role of network topology in accurately predicting perturbation patterns in biological network. Indeed, the development of high-throughput technologies has allowed mapping a significant proportion of interactions between biochemical entities in the cell. In short, we begin to have a good mapping of the subcellular “interacotme”. However, it is unclear how much information is lost given the lack of measurements on the kinetic parameters governing the dynamics of these interactions. Using biochemical networks with experimentally measured kinetic parameters, we show that a knowledge of the network topology offers 65–80% accuracy in predicting the impact of perturbation patterns. In other words, we can use the increasingly accurate topological models to approximate perturbation patterns, bypassing expensive kinetic constant measurement. These results could open new avenues in modeling drug action and in identifying drug targets relying on the human interactome only.

This paper (pdf) is a result of a collaboration between the Sharma Lab at Harvard Medical School and the Renz group at Philipps University Marburg. In this work, I developed IDEAL, a method to predict the role of micro-RNAs (miRNAs) in a disease based on their topological impact in the interactome, and not on their fold-change. The method was applied in the case of asthma, based on an experimental setup and validation done by Ayşe Kılıç (a massive work!). We found that a cocktail of 5 miRNAs identified as having large topological impact, but not large expression fold-change, led to a sharp reduction of the asthmatic Th2 phenotype.

The network medicine satellite “NetMed18: Personalized Medicine in the Era of Big Data” at the NetSci18 conference was a success! With talks ranging from interactome and patient network approaches, social media and word embedding, brain stimulation, genetics and molecular biology… The multi-faceted approaches to network medicine were well represented! Can’t wait for next year 🙂