# Computational Design Engine for Accurate and Efficient Sequencing of DNA and RNA

> **NIH NIH R01** · UNIVERSITY OF MASSACHUSETTS AMHERST · 2020 · $348,054

## Abstract

PROJECT SUMMARY
 The need to develop low-cost, rapid, and high-quality technologies for sequencing mammalian-
sized genomes has inspired many nanopore-based methods. All of these methods suffer from two
huge bottlenecks, which prohibit the required precision, and hence hinder the adoption of any of
these methods as a practical technology as of today. These bottlenecks are: (1) undesirable noise
levels for positioning DNA bases at read-out positions, and (2) difficulty in controlling capture
of large DNA molecules at the nanopore. By tackling these two critical challenges, we propose
to build a Computational Design Engine (CDE) to enable sequencing of DNA and RNA at the
maximum accuracy allowed by laws of physics.
 The first aim is to reduce the positional noise of bases as DNA is being read. This goal will
be accomplished by innovative implementation of ideas based on stochastic resonance, ratchet
rectification, protein-assisted noise reduction, and non-enzymatic electrostatic traps. The proposed CDE will be able to design optimum features of AC fields, on top of ratcheting forces from enzymes and voltage gradients.
 The second aim is to enhance capture of very large DNA and RNA molecules at the nanopore
for subsequent sequencing. The construction of the engine will incorporate all critical components
contributing to capture: entropic barriers, internal structures of RNA, entanglement effects of
DNA, electrostatics, electrohydrodynamics, and nanofluidics. The engine will design the best
experimental protocols, by optimum combinations of various contributing forces, to regulate the
capture efficiency of very large DNA and RNA.
 For both aims, a broad suite of multi-scale modeling, and advanced theories of polymer physics
and non-equilibrium thermodynamics, will be used in innovative ways. The proposed CDE will put
theoretical bounds, based on sound laws of polymer physics, on sequencing accuracy in various
methods being pursued and how to attain their maximum capacities, and to designing better
alternative technologies.

## Key facts

- **NIH application ID:** 9935112
- **Project number:** 5R01HG002776-16
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS AMHERST
- **Principal Investigator:** MURUGAPPAN MUTHUKUMAR
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $348,054
- **Award type:** 5
- **Project period:** 2003-06-06 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9935112

## Citation

> US National Institutes of Health, RePORTER application 9935112, Computational Design Engine for Accurate and Efficient Sequencing of DNA and RNA (5R01HG002776-16). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9935112. Licensed CC0.

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