MCMC Can Detect Nonidentifiable Models

Siekmann, Ivo; Sneyd, James; Crampin, Edmund J.

doi:10.1016/j.bpj.2012.10.024

Cited by 91 publications

(133 citation statements)

References 21 publications

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“…While Aggregated Markov Model (AMM), which has been adopted in ion-channel community for time trace analysis of varying current [62][63][64][65][66][67][68][69][70][71][72][73][74][75][76], can be employed to analyze our data with dynamic disorder, DCMM is better in correctly decoding dynamic disorder than AMM. We found that AMM is prone to overpredict the transition between kinetic patterns (S25 Fig). Our method is more suitable to the data showing persistent dynamic patterns by suppressing unwanted frequent transition between kinetic patterns.…”

Section: Contributions Of Our Workmentioning

confidence: 99%

Decoding Single Molecule Time Traces with Dynamic Disorder

Hwang

Lee²,

Hong³

et al. 2016

Preprint

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Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm-Variational Bayes-double chain Markov model (VB-DCMM)-to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na + solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA. Author SummaryWe have developed a new algorithm to better decode single molecule data with dynamic disorder. Our new algorithm, which represents a substantial improvement over other methodologies, can detect the presence of dynamic disorder in each trajectory and quantify the kinetic characteristics of underlying energy landscape. As a model system, we applied our algorithm to the single molecule FRET time traces of H-DNA. While duplextriplex transitions of H-DNA are conventionally interpreted in terms of two-state kinetics, slowly varying dynamic patterns corresponding to hidden internal states can also be identified from the individual time traces. Our algorithm reveals that at least 4 distinct internal states are required to correctly interpret the data. PLOS Computational Biology |

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Section: Contributions Of Our Workmentioning

confidence: 99%

Decoding Single Molecule Time Traces with Dynamic Disorder

Hwang

Lee²,

Hong³

et al. 2016

Preprint

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show abstract

“…Furthermore, for complex and highly parameterized models, determining whether or not a model is structurally identifiable (i.e., if given the available data, model parameters are able, even in principle, to be uniquely determined) is challenging (47).…”

Section: Statistical Approaches For Estimation Of the Biological Paramentioning

confidence: 99%

Making the Most of Clinical Data: Reviewing the Role of Pharmacokinetic-Pharmacodynamic Models of Anti-malarial Drugs

Simpson

Zaloumis

Livera

et al. 2014

AAPS J

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Abstract. Mechanistic within-host models integrating blood anti-malarial drug concentrations with the parasite-time profile provide a valuable decision tool for determining dosing regimens for anti-malarial treatments, as well as a formative component of population-level drug resistance models. We reviewed published anti-malarial pharmacokinetic-pharmacodynamic models to identify the challenges for these complex models where parameter estimation from clinical field data is limited. The inclusion of key pharmacodynamic processes in the mechanistic structure adopted varies considerably. These include the life cycle of the parasite within the red blood cell, the action of the anti-malarial on a specific stage of the life cycle, and the reduction in parasite growth associated with immunity. With regard to estimation of the pharmacodynamic parameters, the majority of studies simply compared descriptive summaries of the simulated outputs to published observations of host and parasite responses from clinical studies. Few studies formally estimated the pharmacodynamic parameters within a rigorous statistical framework using observed individual patient data. We recommend three steps in the development and evaluation of these models. Firstly, exploration through simulation to assess how the different parameters influence the parasite dynamics. Secondly, application of a simulation-estimation approach to determine whether the model parameters can be estimated with reasonable precision based on sampling designs that mimic clinical efficacy studies. Thirdly, fitting the mechanistic model to the clinical data within a Bayesian framework. We propose that authors present the model both schematically and in equation form and give a detailed description of each parameter, including a biological interpretation of the parameter estimates.

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“…Although parsimony is likely a useful guiding principle, these methods leave us with no rigorous way of quantifying our confidence in models relative to each other; we must rely on ad hoc comparison of models based on Akaike information criterion score or similar methods. Additionally, maximum likelihood methods are generally unable to detect parameter nonidentifiability, where disparate regions of parameter space might yield identical data, a pitfall that is increasingly common as researchers pursue models of higher complexity (13)(14)(15). Although they have been quite useful, likelihood-based approaches for modeling single-molecule time series suffer from important drawbacks.…”

Section: Introductionmentioning

confidence: 98%

Analyzing Single-Molecule Time Series via Nonparametric Bayesian Inference

Hines

Bankston

Aldrich

2015

Biophysical Journal

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The ability to measure the properties of proteins at the single-molecule level offers an unparalleled glimpse into biological systems at the molecular scale. The interpretation of single-molecule time series has often been rooted in statistical mechanics and the theory of Markov processes. While existing analysis methods have been useful, they are not without significant limitations including problems of model selection and parameter nonidentifiability. To address these challenges, we introduce the use of nonparametric Bayesian inference for the analysis of single-molecule time series. These methods provide a flexible way to extract structure from data instead of assuming models beforehand. We demonstrate these methods with applications to several diverse settings in single-molecule biophysics. This approach provides a well-constrained and rigorously grounded method for determining the number of biophysical states underlying single-molecule data.

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MCMC Can Detect Nonidentifiable Models

Cited by 91 publications

References 21 publications

Decoding Single Molecule Time Traces with Dynamic Disorder

Decoding Single Molecule Time Traces with Dynamic Disorder

Making the Most of Clinical Data: Reviewing the Role of Pharmacokinetic-Pharmacodynamic Models of Anti-malarial Drugs

Analyzing Single-Molecule Time Series via Nonparametric Bayesian Inference

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