PLANT: A Method for Detecting Changes of Slope in Noisy Trajectories

Year: 2018

Authors: Sosa-Costa A., Piechocka IK., Gardini L., Pavone FS., Capitanio M., Garcia-Parajo MF., Manzo C.

Autors Affiliation: Barcelona Inst Sci & Technol, ICFO Inst Ciencies Foton, Barcelona, Spain; LENS European Lab Nonlinear Spect, Sesto Fiorentino, Italy; CNR, Natl Inst Opt, Florence, Italy; Univ Florence, Dept Phys & Astron, Sesto Fiorentino, Italy; ICREA, Barcelona, Spain; Univ Vic, Univ Cent Catalunya, Vic, Spain; Polish Acad Sci, Inst Fundamental Technol Res, Warsaw, Poland.

Abstract: Time traces obtained from a variety of biophysical experiments contain valuable information on underlying processes occurring at the molecular level. Accurate quantification of these data can help explain the details of the complex dynamics of biological systems. Here, we describe PLANT (Piecewise Linear Approximation of Noisy Trajectories), a segmentation algorithm that allows the reconstruction of time-trace data with constant noise as consecutive straight lines, from which changes of slopes and their respective durations can be extracted. We present a general description of the algorithm and perform extensive simulations to characterize its strengths and limitations, providing a rationale for the performance of the algorithm in the different conditions tested. We further apply the algorithm to experimental data obtained from tracking the centroid position of lymphocytes migrating under the effect of a laminar flow and from single myosin molecules interacting with actin in a dual-trap force-clamp configuration.

Journal/Review: BIOPHYSICAL JOURNAL

Volume: 114 (9)      Pages from: 2044  to: 2051

More Information: The authors gratefully acknowledge financial support from the European Commission (FP7-ICT-2011-7, grant number 288263), Erasmus Mundus Doctorate Program Europhotonics (grant number 159224-1-2009-1-FR-ERA MUNDUS-EMJD), Spanish Ministry of Economy and Competitiveness (Severo Ochoa’’ Programme for Centres of Excellence in Research and Development SEV-2015-0522 and FIS2014-56107-R grants), Generalitat de Catalunya through the CERCA program, Italian Ministry of University and Research (Futuro in Ricerca 2013 grant number RBFR13V4M2 and Flagship Project NANOMAX), Fundacio Privada CELLEX (Barcelona), Ente Cassa di Risparmio di Firenze, Human Frontier Science Program (GA RGP0027/2012), and LaserLab Europe 4 (GA 654148). C.M. acknowledges funding from the Spanish Ministry of Economy and Competitiveness and the European Social Fund through the Ramon y Cajal program 2015 (RYC-2015-17896).
KeyWords: Tethered Particle Motion; Change-point Detection; Optical Tweezers; Cell-adhesion; Unknown Point; Single; Myosin; Model; Migration; Tracking
DOI: 10.1016/j.bpj.2018.04.006

Citations: 2
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