Advanced Data Analysis

We are using information theory and hidden Markov models to improve the design and interpretation of single molecule fluorescence measurements.

Single molecule (SM) measurements are rapidly becoming commonplace in research laboratories around the world and are contributing to many areas of investigation because of their ability to provide insight into phenomena that were previously intractable because of the ensemble averaging present in bulk measurements. In particular the dynamics of conformationally heterogeneous systems are benefiting from single-molecule studies. Protein folding and conformational dynamics, enzymology, ribozyme function, bacterial light harvesting, and protein-nucleic acid interactions are just a few examples of complex systems that have benefited from the application of SM techniques. However, the impact of SM results has been mitigated by the lack of uniform data analysis and interpretation. This research focuses on SM fluorescence measurements and how to place the experimental design, analysis, and expectations onto solid statistical and theoretical ground.

We use information theory to determine the fundamental limits of SM experiments. This provides a theoretical framework that can be used for experimental design as it provides the limit of the measurement’s ability to make inferences about the properties of the system. It will also provide the benchmark by which to judge data reduction methods.

We are developing statistically rigorous analysis methods based on hidden Markov models develops the algorithms and core codes to implement statistically rigorous methods of data analysis that allow unbiased estimation of system parameters with accuracy approaching the information theory limit including meaningful uncertainty estimates.

We are implementing these methods as user-oriented additions to common data analysis packages so as to provide useable tools for experimental design and analysis to allow other investigators to exploit these methods for their own research.