Computational study of genetic and epigenetic sensing using multi-layered 2D solid-state nanopores

Jean-Pierre Leburton, University of Illinois at Urbana-Champaign

Usage Details

With the rise of ultra-thin electrically active two-dimensional (2D) materials such as graphene and transition metal dichalcogenides, solid-state nanopores have emerged as a promising next generation DNA sequencing devices aiming at revolutionizing modern medicine by providing cost-effective and fast methods to sequence the genome of individuals. For this purpose, we developed simulation techniques to investigate genetic and epigenetic detection in both graphene and MoS₂ nanopores. In order to increase the signal-to-noise-ratio in the detection process, we propose the implementation of innovative schemes to reduce stochasticity by slowing down and stretching the DNA strand during its translocation through multi-layered 2D solid-state nanopore system. In addition, among the objectives of our recent Illinois Proof-of-Concept award by the Illinois Office of Technology Management, and the CompGen fellowship to one of our students from the Illinois Office of the Vice-Chancellor for Research is the development of statistical signal processing algorithms in collaboration with Prof. Lav Varshney at the Coordinate Sciences Laboratory, Illinois to recognize and distinguish DNA nucleotides and various methylated sites on the DNA strands. This will be achieved through various scenarios involving detection by ionic current through the pore and the transverse electronic current across the membrane. The current signatures are obtained by coupling MD simulations to a combination of self-consistent Poisson Boltzmann electrostatics and electronic transport calculations based on non-equilibrium Green's function. To successfully carry out the intended goals of this project, we estimate our usage to be around 836,000 SUs. This study provides the next step towards accurate and rapid DNA-based molecular detection with much higher resolution than with existing models.