Joint Bayesian analysis of single-molecule colocalization images and kinetics

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Project Summary A central concern of the present post-genomic era of biology is understanding at the molecular level the chemical and physical mechanisms by which the protein and RNA machines that perform all cellular functions operate. Multi-wavelength single-molecule fluorescence co-localization techniques (“CoSMoS”; co-localization single-molecule spectroscopy) methods have been widely adopted and used to elucidate the functional mechanisms of a broad range of macromolecular machines ranging from individual motor enzymes to the ribosome and spliceosome. However, efficient and accurate CoSMoS data analysis, particularly of large, multi-dimensional datasets, remains challenging. CoSMoS datasets are inherently difficult to analyze because observations of thermally-driven single-molecule processes at the limited excitation intensities needed to avoid photobleaching are intrinsically noisy and stochastic and thus would benefit from objective methods based on optimized statistical theory to derive accurate conclusions. This application proposes a new approach to CoSMoS data analysis based on Bayesian image classification, Bayesian Markov chain Monte Carlo, and other statistics-based methods. The overall project goal is to produce analytical methods that are more accurate than existing approaches, readily scalable to large datasets, and are more reliable, even in the hands of less experienced users. In particular, we will develop algorithms and implement software that will 1) make full use of the information contained in the two- dimensional CoSMoS images, 2) use objective, statistically rigorous approaches to calculate the probability of a given molecular species being present in each image, 3) integrate kinetic analysis with image classification to allow the most accurate conclusions about molecular mechanisms based on available data, and 4) eliminate the manual analysis and subjective parameter tweaking that introduce bias in existing analytical methods. The developed models and algorithms will be refined and validated through thorough testing against a broad range of simulated and known-outcome empirical data sets. The specific aims of the project are to: 1) enhance the Time-Independent Bayesian Spot Discrimination algorithm and characterize its performance, 2) develop, implement and characterize a time-dependent Joint Bayesian Discrimination/Hidden Markov Modeling (BD/HMM) algorithm to derive molecular mechanisms from CoSMoS data, and 3) develop and distribute a usable, documented, open-source software package for Bayesian CoSMoS image analysis.