Machine learning opens a doorway for microrheology with optical tweezers in living systems

Smith, M. G., Radford, J. , Febrianto, E. , Ramírez, J., O'Mahony, H., Matheson, A. B., Gibson, G. M. , Faccio, D. and Tassieri, M. (2023) Machine learning opens a doorway for microrheology with optical tweezers in living systems. AIP Advances, 13(7), 075315. (doi: 10.1063/5.0161014)

Abstract

It has been argued that linear microrheology with optical tweezers (MOT) of living systems “is not an option” because of the wide gap between the observation time required to collect statistically valid data and the mutational times of the organisms under study. Here, we have explored modern machine learning (ML) methods to reduce the duration of MOT measurements from tens of minutes down to one second by focusing on the analysis of computer simulated experiments. For the first time in the literature, we explicate the relationship between the required duration of MOT measurements (Tm) and the fluid relative viscosity (ηr) to achieve an uncertainty as low as 1% by means of conventional analytical methods, i.e., Tm≅17η3r minutes, thus revealing why conventional MOT measurements commonly underestimate the materials’ viscoelastic properties, especially in the case of high viscous fluids or soft-solids. Finally, by means of real experimental data, we have developed and corroborated an ML algorithm to determine the viscosity of Newtonian fluids from trajectories of only one second in duration, yet capable of returning viscosity values carrying an error as low as ∼0.3% at best, hence opening a doorway for MOT in living systems.

• Item Type: Articles
• Additional Information: This work was supported by the EPSRC CDT in “Intelligent Sensing and Measurement” (EP/L016753/1). M.T. and A.B.M. acknowledge support via the EPSRC grant “Experiencing the micro-world - a cell’s perspective (Grant Nos. EP/R035067/1, EP/R035563/1, and EP/R035156/1)”.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Gibson, Dr Graham and Smith, Matthew and Febrianto, Dr Eky and Tassieri, Dr Manlio and Faccio, Professor Daniele and Radford, Jack
• Authors: Smith, M. G., Radford, J., Febrianto, E., Ramírez, J., O'Mahony, H., Matheson, A. B., Gibson, G. M., Faccio, D., and Tassieri, M.
• College/School: College of Science and Engineering > School of Engineering > Biomedical Engineering; College of Science and Engineering > School of Engineering > Infrastructure and Environment; College of Science and Engineering > School of Physics and Astronomy
• Journal Name: AIP Advances
• Publisher: American Institute of Physics
• ISSN: 2158-3226
• ISSN (Online): 2158-3226
• Published Online: 13 July 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in AIP Advances 13(7): 075315
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
190828		EPSRC Centre for Doctoral Training in Sensing and Measurement	Andrew Harvey	Engineering and Physical Sciences Research Council (EPSRC)	EP/L016753/1	P&S - Physics & Astronomy
301441		Experiencing the micro-world - a cell's perspective	Manlio Tassieri	Engineering and Physical Sciences Research Council (EPSRC)	EP/R035067/1	ENG - Biomedical Engineering