We live in the era of BIG data, machine learning, and artificial intelligence. But sometimes you just don’t have the kind of data you want and gathering more data is challenging and resource intensive. What little you have does constrain mechanistic possibilities.
We define those possibilities in biological systems.
Model definition, model selection, parameter inference, and hypothesis formulation.
We integrate prior knowledge with qualitative, heterogeneous, and scarce data to point our biological collaborators in the direction of accelerated discovery via model-driven, targeted, experimental strategy.
From membrane protein dynamics to systems pharmacology, forensic isotopes, and plant physiology, we are broadly curious and multiscale in our approaches.
Trainees are chemical engineers, biochemists, electrical engineers, mathematicians, chemists, biologists, and physicists. Input from all these disciplines makes the magic happen and makes for a fun space where we learn to speak each other’s scientific languages and build on our collective expertise as a transdisciplinary team.