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The unrooted set covering connected subgraph problem differentiating between HIV envelope sequences

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  • Maher, Stephen J.
  • Murray, John M.

Abstract

This paper presents a novel application of operations research techniques to the analysis of HIV Env gene sequences, aiming to identify key features that are possible vaccine targets. These targets are identified as being critical to the transmission of HIV by being present in early transmitted (founder) sequences and absent in later chronic sequences. Identifying the key features of Env involves two steps: first, calculating the covariance of amino acid combinations and positions to form a network of related and compensatory mutations; and second, developing an integer program to identify the smallest connected subgraph of the constructed covariance network that exhibits a set covering property. The integer program developed for this analysis, labelled the unrooted set covering connected subgraph problem (USCCSP), integrates a set covering problem and connectivity evaluation, the latter formulated as a network flow problem. The resulting integer program is very large and complex, requiring the use of Benders’ decomposition to develop an efficient solution approach. The results will demonstrate the necessity of applying acceleration techniques to the Benders’ decomposition solution approach and the effectiveness of these techniques and heuristic approaches for solving the USCCSP.

Suggested Citation

  • Maher, Stephen J. & Murray, John M., 2016. "The unrooted set covering connected subgraph problem differentiating between HIV envelope sequences," European Journal of Operational Research, Elsevier, vol. 248(2), pages 668-680.
  • Handle: RePEc:eee:ejores:v:248:y:2016:i:2:p:668-680
    DOI: 10.1016/j.ejor.2015.07.011
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    References listed on IDEAS

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    1. Conrad, Jon M. & Gomes, Carla P. & van Hoeve, Willem-Jan & Sabharwal, Ashish & Suter, Jordan F., 2012. "Wildlife corridors as a connected subgraph problem," Journal of Environmental Economics and Management, Elsevier, vol. 63(1), pages 1-18.
    2. John M Murray & Rémy Moenne-Loccoz & Aurélie Velay & François Habersetzer & Michel Doffoël & Jean-Pierre Gut & Isabel Fofana & Mirjam B Zeisel & Françoise Stoll-Keller & Thomas F Baumert & Evelyne Sch, 2013. "Genotype 1 Hepatitis C Virus Envelope Features That Determine Antiviral Response Assessed through Optimal Covariance Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-9, June.
    3. Rodolfo Carvajal & Miguel Constantino & Marcos Goycoolea & Juan Pablo Vielma & Andrés Weintraub, 2013. "Imposing Connectivity Constraints in Forest Planning Models," Operations Research, INFORMS, vol. 61(4), pages 824-836, August.
    4. Santoso, Tjendera & Ahmed, Shabbir & Goetschalckx, Marc & Shapiro, Alexander, 2005. "A stochastic programming approach for supply chain network design under uncertainty," European Journal of Operational Research, Elsevier, vol. 167(1), pages 96-115, November.
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    Cited by:

    1. Stephen J. Maher, 2021. "Enhancing large neighbourhood search heuristics for Benders’ decomposition," Journal of Heuristics, Springer, vol. 27(4), pages 615-648, August.
    2. Duijzer, Lotty Evertje & van Jaarsveld, Willem & Dekker, Rommert, 2018. "Literature review: The vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 268(1), pages 174-192.
    3. Ece Zeliha Demirci & Nesim Kohen Erkip, 2020. "Designing intervention scheme for vaccine market: a bilevel programming approach," Flexible Services and Manufacturing Journal, Springer, vol. 32(2), pages 453-485, June.

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