Learning and Estimation of Single Molecule Behavior

Sivaraman Rajaganapathy, James Melbourne, Tanuj Aggarwal, Rachit Shrivastava, Murti V. Salapaka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Data analysis in single molecule studies often involves estimation of parameters and the detection of abrupt changes in measured signals. For single molecule studies, tools for automated analysis that are crucial for rapid progress, need to be effective under large noise magnitudes, and often must assume little or no prior knowledge of parameters being studied. This article examines an iterated, dynamic programming based step detection algorithm (SDA). It is established that given a prior estimate, an iteration of the SDA necessarily improves the estimate. The analysis provides an explanation and a confirmation of the effectiveness of the learning and estimation capabilities of the algorithm observed empirically. Further, an alternative application of the SDA is demonstrated, wherein the parameters of a worm-like chain (WLC) model are estimated, for the automated analysis of data from single molecule protein pulling experiments.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)9781538654286
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2018 Annual American Control Conference, ACC 2018
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2018 AACC.


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