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Qfold: a new modeling paradigm for the RNA folding problem

Author

Listed:
  • Mark W. Lewis

    (Missouri Western State University)

  • Amit Verma

    (Missouri Western State University)

  • Todd T. Eckdahl

    (Missouri Western State University)

Abstract

Ribonucleic acid (RNA) molecules play informational, structural, and metabolic roles in all living cells. RNAs are chains of nucleotides containing bases {A, C, G, U} that interact via base pairings to determine higher order structure and functionality. The RNA folding problem is to predict one or more secondary RNA structures from a given primary sequence of bases. From a mathematical modeling perspective, solutions to the RNA folding problem come from minimizing the thermodynamic free energy of a structure by selecting which bases will be paired, subject to a set of constraints. Here we report on a Quadratic Unconstrained Binary Optimization (QUBO) modeling paradigm that fits naturally with the parameters and constraints required for RNA folding prediction. Three QUBO models are presented along with a hybrid metaheuristic algorithm. Extensive testing results show a strong positive correlation with benchmark results.

Suggested Citation

  • Mark W. Lewis & Amit Verma & Todd T. Eckdahl, 2021. "Qfold: a new modeling paradigm for the RNA folding problem," Journal of Heuristics, Springer, vol. 27(4), pages 695-717, August.
    • Handle: RePEc:spr:joheur:v:27:y:2021:i:4:d:10.1007_s10732-021-09471-3
    DOI: 10.1007/s10732-021-09471-3
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    References listed on IDEAS

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    1. Mauri, Geraldo Regis & Lorena, Luiz Antonio Nogueira, 2012. "A column generation approach for the unconstrained binary quadratic programming problem," European Journal of Operational Research, Elsevier, vol. 217(1), pages 69-74.
    2. Mark Lewis & Gary Kochenberger, 2016. "Probabilistic multistart with path relinking for solving the unconstrained binary quadratic problem," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 26(1), pages 13-33.
    3. Gary Kochenberger & Jin-Kao Hao & Fred Glover & Mark Lewis & Zhipeng Lü & Haibo Wang & Yang Wang, 2014. "The unconstrained binary quadratic programming problem: a survey," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 58-81, July.
    4. Jaswinder Singh & Jack Hanson & Kuldip Paliwal & Yaoqi Zhou, 2019. "RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    5. Fred Glover & Gary Kochenberger & Yu Du, 2019. "Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models," 4OR, Springer, vol. 17(4), pages 335-371, December.
    6. Glover, Fred & Alidaee, Bahram & Rego, Cesar & Kochenberger, Gary, 2002. "One-pass heuristics for large-scale unconstrained binary quadratic problems," European Journal of Operational Research, Elsevier, vol. 137(2), pages 272-287, March.
    7. Wang, Yang & Lü, Zhipeng & Glover, Fred & Hao, Jin-Kao, 2012. "Path relinking for unconstrained binary quadratic programming," European Journal of Operational Research, Elsevier, vol. 223(3), pages 595-604.
    8. Glover, Fred & Lewis, Mark & Kochenberger, Gary, 2018. "Logical and inequality implications for reducing the size and difficulty of quadratic unconstrained binary optimization problems," European Journal of Operational Research, Elsevier, vol. 265(3), pages 829-842.
    9. Francisco Barahona & Martin Grötschel & Michael Jünger & Gerhard Reinelt, 1988. "An Application of Combinatorial Optimization to Statistical Physics and Circuit Layout Design," Operations Research, INFORMS, vol. 36(3), pages 493-513, June.
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