We're a team of physicists, engineers, and biologists. We investigate the organization and dynamics of single molecules in living cells using new optical tools. In these studies, we collect time-series data and then analyze them to find underlying regulatory and organizational principles. Our lab is located in the Shriram Center.
Beyond 'basic' biophysics, we also try to tackle broader problems in medicine. To help bring together patients and engineers, and to create training sets for artificial intelligence and machine learning, we recently co-founded CancerBase, a place where patients can share medical data and learn from one-another. We think of CancerBase as an early example for how the internet can reshape healthcare in the US and elsewhere.
Imagine you are an orchestra conductor directing a symphony. If you're good at what you do, everything will sound right. How does the genome solve the equivalent problem, except without a conductor? We use genome-edited cell lines to investigate how DNA-looping and chromatin compaction influence transcriptional regulation. The image shows a single nucleus; The DNA is blue, single RNA transcripts are red/yellow.
We use concepts and tools from non-equilibrium statistical mechanics, machine learning, and polymer physics to model and explore biological processes. Increasingly, we use convolutional neural nets to make sense of image data. Our DeepEvolve python/keras framework for evolving CNN hyperparameters is on Github.
We use single-molecule tracking to learn how the NPC controls access to the nucleus. The image shows a schematic of a NPC in the nuclear membrane, and a single cargo transiting the pore. The NPC is both highly selective and efficient; our goal is to understand how the pore implements those apparently conflicting goals. Moreover, we would like to clarify the fundamental basis for the pore's ability to efficiently rectify molecular transport.