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Modelling and Optimisation of a DNA Stack Nano-Device Using Probabilistic Model Checking

Authors Bowen Li , Neil Mackenzie, Ben Shirt-Ediss , Natalio Krasnogor , Paolo Zuliani

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Author Details

Bowen Li
  • Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
Neil Mackenzie
  • Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
Ben Shirt-Ediss
  • Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
Natalio Krasnogor
  • Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
Paolo Zuliani
  • Interdisciplinary Computing and Complex bioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK

Funding

This work was partly funded by the EPSRC’s project "Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies" (EP/N031962/1). Krasnogor was supported by the Royal Academy of Engineering under the Chairs in Emerging Technologies scheme.

Acknowledgements

The authors would like to thank Dr. Harold Fellermann for helpful advice.

Cite AsGet BibTex

Bowen Li, Neil Mackenzie, Ben Shirt-Ediss, Natalio Krasnogor, and Paolo Zuliani. Modelling and Optimisation of a DNA Stack Nano-Device Using Probabilistic Model Checking. In 28th International Conference on DNA Computing and Molecular Programming (DNA 28). Leibniz International Proceedings in Informatics (LIPIcs), Volume 238, pp. 5:1-5:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/LIPIcs.DNA.28.5

Abstract

A DNA stack nano-device is a bio-computing system that can read and write molecular signals based on DNA-DNA hybridisation and strand displacement. In vitro implementation of the DNA stack faces a number of challenges affecting the performance of the system. In this work, we apply probabilistic model checking to analyse and optimise the DNA stack system. We develop a model framework based on continuous-time Markov chains to quantitatively describe the system behaviour. We use the PRISM probabilistic model checker to answer two important questions: 1) What is the minimum required incubation time to store a signal? And 2) How can we maximise the yield of the system? The results suggest that the incubation time can be reduced from 30 minutes to 5-15 minutes depending on the stack operation stage. In addition, the optimised model shows a 40% increase in the target stack yield.

Subject Classification

ACM Subject Classification
  • Theory of computation → Logic
Keywords
  • probabilistic model checking
  • CTMC
  • DNA computing
  • DNA stack

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Supplementary Materials

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