Single molecule experiments have opened new windows to molecular biology by allowing us to follow individual proteins at work both in vitro and in vivo.
However, instrumental noise, cross-sample variations, and the inherent randomness of Brownian motion and low copy number chemistry often makes for noisy data that can be challenging to interpret. A very common problem is to analyze noisy time series with abrupt changes, reflecting for example binding events, or conformational changes in a protein complex.
In this talk, I will describe my ongoing attempts to tackle such problems with a combination of physical modeling and a machine learning technique known as ensemble learning or variational Bayes. In particular, I will discuss an application to in vitro DNA looping by the Lac repressor protein.
