An Introduction to Infinite HMMs for Single-Molecule Data Analysis

Sgouralis, Ioannis; Pressé, Steve

doi:10.1016/j.bpj.2017.04.027

Cited by 91 publications

(181 citation statements)

References 47 publications

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“…In summary, we have presented a generalizable approach that extends the potential applicability of HMMs for SPT data. More broadly, we have shown a novel application of the iHMM method, further proving that it is an excellent tool for quantification of experimental biophysics data [26]. All experimental datasets and software are available from the authors on request.…”

Section: Discussionmentioning

confidence: 73%

“…Finally, we resample the parametersβ,π s i , and the emission parameters: ν σ k . There are many more technicalities in the general method that one can find in [26].…”

Section: Infinite Hidden Markov Model For Spt (Ihmmspt)mentioning

confidence: 99%

“…Here, we will attack the problem using a Bayesian nonparametric approach that allows the parameter space to be infinite-dimensional. The so-called Infinite Hidden Markov Model (iHMM) is a nonparametric model [25] that has recently been applied to FRET data by Pressé and coworkers [26,27] to estimate the number of conformations of a molecule and simultaneously infer kinetic parameters for each conformational state. We use these concepts to develop a novel tool to analyze single particle tracking data under the assumption that the trajectories follow a Markov chain, where each element of the chain is a diffusion process.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we will begin by specifying the HMM model that we wish to apply to SPT data, and then follow the thorough exposition of Sgouralis et al [26] to generalize to the infinitedimensional (iHMM) setting. We show validation using simulated data and provide a technical improvement to the algorithm that assists with convergence for the problem at hand.…”

Section: Introductionmentioning

confidence: 99%

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Diffusion analysis of single particle trajectories in a Bayesian nonparametrics framework

Falcão

Coombs

2019

Preprint

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Single particle tracking (SPT), where individual molecules are fluorescently labelled and followed over time, is an important tool that allows the spatiotemporal dynamics of subcellular biological systems to be studied at very fine temporal and spatial resolution. Mathematical models of particle motion are typically based on Brownian diffusion, reflecting the noisy environment that biomolecules typically inhabit. In order to study changes in particle behaviour within individual tracks, Hidden Markov models (HMM) featuring multiple diffusive states have been used as a descriptive tool for SPT data. However, such models are typically specified with an a-priori defined number of particle states and it has not been clear how such assumptions have affected their outcomes. Here, we propose a method for simultaneously inferring the number of diffusive states alongside the dynamic parameters governing particle motion. Our method is an infinite HMM (iHMM) within the general framework of Bayesian non-parametric models. We directly extend previous applications of these concepts in molecular biophysics to the SPT framework and propose and test an additional constraint with the goal of accelerating convergence and reducing computational time. We test our iHMM using simulated data and apply it to a previously-analyzed large SPT dataset for B cell receptor motion on the plasma membrane of B cells of the immune system.

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Section: Discussionmentioning

confidence: 73%

“…Finally, we resample the parametersβ,π s i , and the emission parameters: ν σ k . There are many more technicalities in the general method that one can find in [26].…”

Section: Infinite Hidden Markov Model For Spt (Ihmmspt)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diffusion analysis of single particle trajectories in a Bayesian nonparametrics framework

Falcão

Coombs

2019

Preprint

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“…While BNPs have had a deep impact on Data Sci-ence since their inception, they are relatively new to Biophysics with a handful of papers [18,47,73] published to date using BNPs in Biophysical applications [50,55,[87][88][89][90]92].…”

Section: Introductionmentioning

confidence: 99%

Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Tavakoli

Jazani

Sgouralis

et al. 2019

Preprint

Self Cite

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Fluorescence time traces are used to report on dynamical properties of biomolecules. The basic unit of information drawn from these traces is the individual photon. Single photons carry instantaneous information from the biomolecule, from which they are emitted, to the detector on timescales as fast as microseconds. Thus from confocal microscope it is theoretically possible to monitor biomolecular dynamics at such timescales. In practice, however, signals are stochastic and in order to deduce dynamical information through traditional means-such as fluorescence correlation spectroscopy (FCS) and related techniques-fluorescence signals are collected and temporally auto-correlated over several minutes. So far, it has been impossible to analyze dynamical properties of biomolecules on timescales approaching data acquisition as this demands that we estimate the instantaneous number of biomolecules emitting photons and their positions within the confocal volume. The mathematical structure of this problem demands that we abandon the normal ("parametric") Bayesian paradigm. Here, we exploit novel mathematical tools, namely Bayesian nonparametrics, that allow us to deduce in a principled fashion the same information normally deduced from FCS but from the direct analysis of significantly smaller datasets starting from individual photon arrivals. We discuss the implications of this method in helping dramatically reduce phototoxic damage on the sample and the ability to monitor out-of-equilibrium processes.

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Kinetics of Palladium(0)‐Allyl Interactions in the Tsuji‐Trost Reaction, derived from Single‐Molecule Fluorescence Microscopy

et al. 2020

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Single-molecule (SM) chemistry is devoted to unravel reaction steps which are hidden in cuvette experiments. Controversies about the substrate activation during the Tsuji-Trost deallylation motivated us to study, on the single-molecule level, the kinetics of the catalyst precursor Pd(PPh 3 ) 4 with our recently designed two-color fluorescent probes. Photochemical, metal-free bypass reactions were found and taken into account by the combina-tion of spectrally separated single-molecule TIRF-microscopy and state-of-the art analysis procedures. Unselective π-complex formation (K D � 10 3 M À 1 ) precedes the insertion of the active catalyst into the CÀ OR bond (RO À = leaving group), indicated by the lacking immediate change of fluorescence color. The formed intermediate then decomposes on a time scale of � 2 -3 s to the deallylated product.

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An Introduction to Infinite HMMs for Single-Molecule Data Analysis

Cited by 91 publications

References 47 publications

Diffusion analysis of single particle trajectories in a Bayesian nonparametrics framework

Diffusion analysis of single particle trajectories in a Bayesian nonparametrics framework

Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Kinetics of Palladium(0)‐Allyl Interactions in the Tsuji‐Trost Reaction, derived from Single‐Molecule Fluorescence Microscopy

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