Monte Carlo DNA Simulator

By Alena Bulyha1, Clemens Heitzinger2

1. University of Vienna 2. Purdue University

Simulate ionic concentration profiles at charged boundaries functionalized with DNA oligomers.

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Version 1.1 - published on 22 Sep 2014

doi:10.4231/D36T0GX1N cite this

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The MCDNA tool calculates ionic (Na and Cl) concentration profiles in electrolytes between two charged surfaces. A voltage difference between the two electrodes can be applied and one of the electrodes can be functionalized with different types of biomolecules. The leading application are the surfaces of BioFETs (biologically sensitive field-effect transistors). The simulations are three-dimensional Metropolis Monte-Carlo calculations in the constant-voltage ensemble. One of the surfaces can be functionalized with PNA (peptide nucleic acid), ssDNA (single-stranded DNA), or dsDNA (double-stranded DNA) oligomers. In addition to the concentration profiles, other quantities of interest such as the chemical potential, the surface charge density, and the dipole moment density of the boundary layer at the functionalized surface are calculated. The whole system is electrically neutral and periodic in the two coordinate directions of the parallel surfaces. The density of the biomolecules at the surface can be adjusted via their distance. A simulation box can contain several molecules, since otherwise the number of ions in a simulation box would be too small for meaningful calculations at low ionic concentrations. If there is a single molecule in the simulation box, it is linked at the center of the lower electrode. If the simulation box contains more molecules, they are arranged in a square grid and each molecule is centered in its grid cell. Each oligomer is bound to the surface by a linker. The PNA and DNA oligomers and their linkers are modeled as impenetrable cylinders with two hemispheres of the same radius at the top and at the bottom. PNA oligomers are modeled by uncharged cylinders, and ssDNA and dsDNA oligomers carry the charges of the phosphate groups of the backbone on their outside just as in B-DNA oligomers. The linkers are orthogonal to the surface so that they touch the surface. The upper hemisphere of the linker overlaps with the lower hemisphere of the oligomer and acts as a flexible joint. Hence the oligomers can be rotated with respect to the surface. The length of the linkers and the number of nucleotides in the oligomers, the number of oligomers in the simulation box, the distance between them, and their angle with respect to the surface plane are parameters of the simulation. Furthermore, the ionic concentration and the distance between the two electrodes must be specified. The distance between the two electrodes, i.e., the height of simulation box, and the applied voltage yield the electric field in the simulation box. The accuracy of the Monte-Carlo simulations is determined by the algorithmic parameters. The default algorithmic parameters yield fast results with relatively small noise.


The simulator is based on Monte-Carlo code provided by Prof. Dezső Boda (Department of Physical Chemistry, University of Pannonia).

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The development of this simulator has been supported by research projects funded by the Austrian Academy of Sciences (ÖAW) and the Austrian Science Fund (FWF).


  1. A. Bulyha and C. Heitzinger (2011) An algorithm for three-dimensional Monte Carlo simulation of charge distribution at biofunctionalized surfaces. Nanoscale, 3(4), pp. 1608-1617, DOI: 10.1039/C0NR00791A. [Abstract]
  2. S. Baumgartner, M. Vasicek, A. Bulyha, and C. Heitzinger (2011) Optimization of nanowire DNA sensor sensitivity using self-consistent simulation. Nanotechnology, 22(42), 425503/1-8. [Abstract]
  3. S. Baumgartner, M. Vasichek, A. Bulyha, N. Tassoti and C. Heitzinger (2011) Analysis of Field-Effect Biosensors using Self-Consistent 3D Drift-Diffusion and Monte-Carlo Simulations. Procedia Engineering, 25, 407-410. [Abstract]

Cite this work

Researchers should cite this work as follows:

  • Alena Bulyha, Clemens Heitzinger (2014), "Monte Carlo DNA Simulator," (DOI: 10.21981/D36T0GX1N).

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