At a time when many are wrangling with biological ‘big data’, there remain important problems that are fundamentally data limited – often physiological questions for which there is little quantitative data, and further data collection may be hampered by limited resources, ethical constraints, or simply a lack of clarity as to which measurements are most likely to shed light on mechanisms of interest. What little you have does constrain mechanistic possibilities.
We use computational models to formally define those possibilities in dynamic 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.