Networks are everywhere helping us understand complex relationships e.g., social networks or networks describing dependencies in supply-chain models. We believe that ever since Euler's solution to the problem of Konigsberg's bridges, networks have become a tool that can bring together people from different disciplines into a ground with common understanding.

We are interested in molecular networks that elucidate the underlying biological mechanisms through depictions of interactions. In particular, we use probabilistic graph representations to model interactions among different molecules. Such an approach not only summarizes the rules that govern biological functions, but also helps us develop putative hypotheses that warrant further research.

We provide a suite of network analysis for high throughput biological data that involves finding pathways that are active for a given experimental condition, identifying novel interaction networks, and defining key molecules based on topology.

We also provide tools that can instantiate and infer learned networks simulating certain experimental conditions without the associated cost or risks.