scholarly journals Error-based Extraction of States and Energy Landscapes from Experimental Single-Molecule Time-Series

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
J. Nicholas Taylor ◽  
Chun-Biu Li ◽  
David R. Cooper ◽  
Christy F. Landes ◽  
Tamiki Komatsuzaki
Keyword(s):  
Time Series ◽  
Single Molecule ◽  
Energy Landscapes

scholarly journals Reconstruction of protein structures from single-molecule time series

2020 ◽  
Vol 153 (19) ◽  
pp. 194102
Author(s):  
Maximilian Topel ◽  
Andrew L. Ferguson
Keyword(s):  
Time Series ◽  
Single Molecule ◽  
Protein Structures

scholarly journals Non-Markovian properties and multiscale hidden Markovian network buried in single molecule time series

2013 ◽  
Vol 139 (24) ◽  
pp. 245101 ◽  
Author(s):  
Tahmina Sultana ◽  
Hiroaki Takagi ◽  
Miki Morimatsu ◽  
Hiroshi Teramoto ◽  
Chun-Biu Li ◽  
...  
Keyword(s):  
Time Series ◽  
Single Molecule

A Forecast of Developments in Scanned Probe Microscopy

2006 ◽  
Vol 59 (6) ◽  
pp. 355 ◽  
Author(s):  
Vincent S. J. Craig ◽  
Tim J. Senden
Keyword(s):  
Single Molecule ◽  
Energy Landscapes ◽  
Probe Microscopy ◽  
Nano Fabrication ◽  
Scanned Probe Microscopy ◽  
Atom Manipulation

From direct atom manipulation and nano-fabrication, to single molecule sensing and probing energy landscapes, the tools of the nanotech age are here. Scanned probe microscopies (SPM) offer opportunities to directly interact with matter in native environments and their evolution shows no signs of slowing. How might this toolkit adapt for new and outstanding problems in science? Here some directions are entertained and potential developments explored.

scholarly journals Unsupervised selection of optimal single-molecule time series idealization criterion

2021 ◽  
Author(s):  
Argha Bandyopadhyay ◽  
Marcel P. Goldschen-Ohm
Keyword(s):  
Time Series ◽  
Single Molecule ◽  
Time Windows ◽  
Underlying Mechanism ◽  
Rapid Analysis ◽  
Model Complexity ◽  
Decision Boundary ◽  
Signal To Noise ◽  
Objective Criterion ◽  
Selection Of

AbstractSingle-molecule (SM) approaches have provided valuable mechanistic information on many biophysical systems. As technological advances lead to ever-larger datasets, tools for rapid analysis and identification of molecules exhibiting the behavior of interest are increasingly important. In many cases the underlying mechanism is unknown, making unsupervised techniques desirable. The Divisive Segmentation and Clustering (DISC) algorithm is one such unsupervised method that idealizes noisy SM time series much faster than computationally intensive approaches without sacrificing accuracy. However, DISC relies on a user selected objective criterion (OC) to guide its estimation of the ideal time series. Here, we explore how different OCs affect DISC’s performance for data typical of SM fluorescence imaging experiments. We find that OCs differing in their penalty for model complexity each optimize DISC’s performance for time series with different properties such as signal-to-noise and number of sample points. Using a machine learning approach, we generate a decision boundary that allows unsupervised selection of OC based on the input time series to maximize performance for different types of data. This is particularly relevant for SM fluorescence datasets which often have signal-to-noise near the derived decision boundary and include time series of nonuniform length due to stochastic bleaching. Our approach allows unsupervised per-molecule optimization of DISC, which will substantially assist rapid analysis of high-throughput single-molecule datasets with noisy samples and nonuniform time windows.

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