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Jonathan E. Bronson

Researcher at Columbia University

Publications -  11
Citations -  891

Jonathan E. Bronson is an academic researcher from Columbia University. The author has contributed to research in topics: Hidden Markov model & Expectation–maximization algorithm. The author has an hindex of 9, co-authored 11 publications receiving 773 citations. Previous affiliations of Jonathan E. Bronson include University of Illinois at Urbana–Champaign.

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Learning Rates and States from Biophysical Time Series: A Bayesian Approach to Model Selection and Single-Molecule FRET Data

TL;DR: It is demonstrated how model selection in such probabilistic or generative modeling can facilitate analysis of closely related temporal data currently prevalent in biophysics, and how this technique can be applied to temporal data such as smFRET time series.
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Empirical Bayes Methods Enable Advanced Population-Level Analyses of Single-Molecule FRET Experiments

TL;DR: This work demonstrates how a recently developed empirical Bayesian method for HMMs can be extended to enable a more automated and statistically principled approach to two widely occurring tasks in the analysis of single-molecule fluorescence resonance energy transfer (smFRET) experiments.
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Allosteric collaboration between elongation factor G and the ribosomal L1 stalk directs tRNA movements during translation

TL;DR: The real-time dynamics of the L1 stalk, a structural element of the large ribosomal subunit that is implicated in directing tRNA movements during translation, are reported and a model for the release of E-site tRNA is suggested.
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Transfer RNA–mediated regulation of ribosome dynamics during protein synthesis

TL;DR: This work used single-molecule FRET to characterize the dynamics of ribosomal pretranslocation (PRE) complex analogs carrying either wild-type or systematically mutagenized tRNAs and suggests that the conformational flexibility of the tRNA molecule has a crucial role in directing the structural dynamics of the PRE complex during translocation.
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Graphical models for inferring single molecule dynamics

TL;DR: In this article, the authors describe how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM) for single-molecule fluorescence resonance energy transfer (smFRET) versus time data, where the smFRET time series is modeled as a HMM with Gaussian observables.