ArticleBiophysical journal2000
Hidden Markov modeling for single channel kinetics with filtering and correlated noise.
Article in Biophysical journal, 2000. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Bayesian inference of functional asymmetry in the homotrimeric ligand-gated ion channel P2XCommunications biology · 2025Article
- Using Bayesian priors to overcome non-identifiablility issues in Hidden Markov models.bioRxiv : the preprint server for biology · 2025Article
- A deep learning approach to real-time Markov modeling of ion channel gating.Communications chemistry · 2024Article
- Estimating the CaMethods in molecular biology (Clifton, N.J.) · 2024Article
- 2D-dwell-time analysis with simulations of ion-channel gating using high-performance computing.Biophysical journal · 2023Article
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- Analysis of patchclamp recordings: model-free multiscale methods and software.European biophysics journal : EBJ · 2021Article
- Moving average filtering with deconvolution (MAD) for hidden Markov model with filtering and correlated noise.European biophysics journal : EBJ · 2019Article
- Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints.The Journal of general physiology · 2018Article
- Computational membrane biophysics: From ion channel interactions with drugs to cellular function.Biochimica et biophysica acta. Proteins and proteomics · 2017Review
- Disruption of a putative intersubunit electrostatic bond enhances agonist efficacy at the human α1 glycine receptor.Brain research · 2017Article
- Is realistic neuronal modeling realistic?Journal of neurophysiology · 2016Review
- Bayesian Statistical Inference in Ion-Channel Models with Exact Missed Event Correction.Biophysical journal · 2016Article
- Hidden Markov analysis of improved bandwidth mechanosensitive ion channel data.European biophysics journal : EBJ · 2015Article
- Analyzing and Quantifying the Gain-of-Function Enhancement of IP3 Receptor Gating by Familial Alzheimer's Disease-Causing Mutants in Presenilins.PLoS computational biology · 2015Article
- Single-molecule dataset (SMD): a generalized storage format for raw and processed single-molecule data.BMC bioinformatics · 2015Article
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- Maximum likelihood estimation of biophysical parameters of synaptic receptors from macroscopic currents.Frontiers in cellular neuroscience · 2014Article
- Error analysis of idealized nanopore sequencing.Electrophoresis · 2013Article
- Signal processing for molecular and cellular biological physics: an emerging field.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2013Article
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3 authors.
Funding
Abstract
Hidden Markov modeling (HMM) can be applied to extract single channel kinetics at signal-to-noise ratios that are too low for conventional analysis. There are two general HMM approaches: traditional Baum's reestimation and direct optimization. The optimization approach has the advantage that it optimizes the rate constants directly. This allows setting constraints on the rate constants, fitting multiple data sets across different experimental conditions, and handling nonstationary channels where the starting probability of the channel depends on the unknown kinetics. We present here an extension of this approach that addresses the additional issues of low-pass filtering and correlated noise. The filtering is modeled using a finite impulse response (FIR) filter applied to the underlying signal, and the noise correlation is accounted for using an autoregressive (AR) process. In addition to correlated background noise, the algorithm allows for excess open channel noise that can be white or correlated. To maximize the efficiency of the algorithm, we derive the analytical derivatives of the likelihood function with respect to all unknown model parameters. The search of the likelihood space is performed using a variable metric method. Extension of the algorithm to data containing multiple channels is described. Examples are presented that demonstrate the applicability and effectiveness of the algorithm. Practical issues such as the selection of appropriate noise AR orders are also discussed through examples.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.