Mechanisms of DNA separation in entropic trap arrays: a Brownian dynamics simulation

Abstract

Using Brownian dynamics simulations, we study the migration of long charged chains in an electrophoretic microchannel device consisting of an array of microscopic entropic traps with alternating deep regions and narrow constrictions. Such a device has been designed and fabricated recently by Han et al. for the separation of DNA molecules (Science, 2000). Our simulation reproduces the experimental observation that the mobility increases with the length of the DNA. A detailed data analysis allows to identify the reasons for this behavior. Two distinct mechanisms contribute to slowing down shorter chains. One has been described earlier by Han et al.: the chains are delayed at the entrance of the constriction and escape with a rate that increases with chain length. The other, actually dominating mechanism is here reported for the first time: Some chains diffuse out of their main path into the corners of the box, where they remain trapped for a long time. The probability that this happens increases with the diffusion constant, i. e., the inverse chain length.

Simulation of DNA electrophoresis in systems of large number of solvent particles by coarse‐grained hybrid molecular dynamics approach

“…(4) and (5), the temperature T can be automatically controlled, the solvent friction is also included, and they can be considered as the extension of Langevin dynamics equation for the beads such that the energy, momentum as well as mass are conserved. 5,14,15,36 The particles (polymer beads and the solvent particles) in our method are coarse-grained. The mass of the polymer bead m b is set as unit mass.…”

“…For example, the bond-fluctuation Monte-Carlo method has been used for simulating the motion of long polyelectrolytes inside an array of microscopic traps. 5 The mobility of DNA and its length-dependent time scale of movement have been studied by Brownian dynamics (BD) simulations. 6,7 These simulation studies have yielded good agreement with observations.…”

“…On the basis of the significant progresses in using implicit solvent models for studying DNA electrophoresis and other processes, 5,[14][15][16] efforts have recently been directed at the simulation of these and other systems of large number of solvent particles with explicit solvent. [17][18][19][20][21][22] In particular, coarse-grained molecular dynamics (CG-MD) with explicit hydrodynamics has been used for studying the friction and the collision of polymers and the tethered polymers in shear flow, [17][18][19] and the dissipative particle dynamics (DPD) method has been used for studying the mobility of DNA electrophoresis in a well-studied nano-fluidic device in explicit solvent.…”

“…22 The bonding and excluded volume interactions between polymer beads are modeled by the finitely extensible nonlinear elastic potential (FENE) and Lennard-Jones potential, 20,[29][30][31] the excluded volume interaction between polymer bead and solvent are described by the Lennard-Jones potential, and the solvent-solvent interaction is represented by a frictional force and random force that obey the fluctuation-dissipation theorem. 5,14,15,32 In this work, we tested whether the CGH-MD is capable of simulating a system of large number of particles in explicit solvent at substantially lower computing cost and at accuracy levels comparable to those of coarse-grained MD. 17,18 Our method was first tested on the simulation of a standard linear polymer solution widely used in evaluating the performance of different computational methods.…”

“…19,33 It was then tested on the computation of nonuniform distribution of ions in a cylindrical nanotube by comparing our result with that of CG-MD. 33,34 Our method was further evaluated by using it to simulate the process of DNA electrophoresis in a polymer solution and in a well-studied nanofluidic device to compare its performance with observations and other simulation studies 5,6 and to evaluate its computing cost with respect to those of other methods.…”