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 language||English (US)|
|Title of host publication||2018 Annual American Control Conference, ACC 2018|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|State||Published - Aug 9 2018|
|Event||2018 Annual American Control Conference, ACC 2018 - Milwauke, United States|
Duration: Jun 27 2018 → Jun 29 2018
|Name||Proceedings of the American Control Conference|
|Other||2018 Annual American Control Conference, ACC 2018|
|Period||6/27/18 → 6/29/18|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT We thank Sayan Ghosal for developing the protocols and providing training for conducting the AFM based force spectroscopy experiments. The work in the article was made possible by NSF, CMMI grant and NSF, CPS grant to MS.
© 2018 AACC.