Stochastic unfolding of nanoconfined DNA: Experiments, model and Bayesian analysis
Journal article, 2018

Nanochannels provide a means for detailed experiments on the effect of confinement on biomacro-molecules, such as DNA. Here we introduce a model for the complete unfolding of DNA from the circular to linear configuration. Two main ingredients are the entropic unfolding force and the friction coefficient for the unfolding process, and we describe the associated dynamics by a non-linear Langevin equation. By analyzing experimental data where DNA molecules are photo-cut and unfolded inside a nanochannel, our model allows us to extract values for the unfolding force as well as the friction coefficient for the first time. In order to extract numerical values for these physical quantities, we employ a recently introduced Bayesian inference framework. We find that the determined unfolding force is in agreement with estimates from a simple Flory-type argument. The estimated friction coefficient is in agreement with theoretical estimates for motion of a cylinder in a channel. We further validate the estimated friction constant by extracting this parameter from DNA's center-of -mass motion before and after unfolding, yielding decent agreement. We provide publically available software for performing the required image and Bayesian analysis. Published by AIP Publishing.

Author

Jens Krog

University of Southern Denmark

Lund University

Mohammadreza Alizadehheidari

Chalmers, Biology and Biological Engineering, Chemical Biology

Erik Werner

University of Gothenburg

Santosh Kumar Bikarolla

Chalmers, Biology and Biological Engineering, Chemical Biology

Jonas O. Tegenfeldt

Lund University

Bernhard Mehlig

University of Gothenburg

Michael A. Lomholt

University of Southern Denmark

Fredrik Westerlund

Chalmers, Biology and Biological Engineering, Chemical Biology

Tobias Ambjornsson

Lund University

Journal of Chemical Physics

0021-9606 (ISSN) 1089-7690 (eISSN)

Vol. 149 21 215101

Subject Categories

Applied Mechanics

Biophysics

Probability Theory and Statistics

DOI

10.1063/1.5051319

PubMed

30525714

More information

Latest update

1/18/2019