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A bi-objective network design approach for discovering functional modules linking Golgi apparatus fragmentation and neuronal death

Author

Listed:
  • Eduardo Álvarez-Miranda

    (Universidad de Talca)

  • Hesso Farhan

    (Biotechnology Institute Thurgau
    University of Konstanz)

  • Martin Luipersbeck

    (University of Vienna)

  • Markus Sinnl

    (University of Vienna)

Abstract

Experimental records show the existence of a biological linkage between neuronal death and Golgi apparatus fragmentation. The comprehension of such linkage should help to understand the dynamics undergoing neurological damage caused by diseases such as Alzheimer’s disease or amyotrophic lateral sclerosis. In this paper, the bi-objective minimum cardinality bottleneck Steiner tree problem along with an ad-hoc exact algorithm are proposed to study such phenomena. The proposed algorithm is based on integer programming and the so-called $$\epsilon $$ ϵ -constraint method. A key feature of the devised approach is that it allows an efficient integer programming formulation of the problem. The obtained results show that it is possible to obtain additional evidence supporting the hypothesis that alterations of the Golgi apparatus structure and neuronal death interact through the biological mechanisms underlying the outbreak and progression of neurodegenerative diseases. Moreover, the function of cellular response to stress as a biological linkage between these phenomena is also further investigated. Complementary, computational results on a synthetic dataset are also provided with the aim of reporting the performance of the proposed algorithm.

Suggested Citation

  • Eduardo Álvarez-Miranda & Hesso Farhan & Martin Luipersbeck & Markus Sinnl, 2017. "A bi-objective network design approach for discovering functional modules linking Golgi apparatus fragmentation and neuronal death," Annals of Operations Research, Springer, vol. 258(1), pages 5-30, November.
    • Handle: RePEc:spr:annopr:v:258:y:2017:i:1:d:10.1007_s10479-016-2188-2
    DOI: 10.1007/s10479-016-2188-2
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    References listed on IDEAS

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    1. Duin, C. W. & Volgenant, A., 1997. "The partial sum criterion for Steiner trees in graphs and shortest paths," European Journal of Operational Research, Elsevier, vol. 97(1), pages 172-182, February.
    2. Altannar Chinchuluun & Panos Pardalos, 2007. "A survey of recent developments in multiobjective optimization," Annals of Operations Research, Springer, vol. 154(1), pages 29-50, October.
    3. Igor Ulitsky & Akshay Krishnamurthy & Richard M Karp & Ron Shamir, 2010. "DEGAS: De Novo Discovery of Dysregulated Pathways in Human Diseases," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-14, October.
    4. Natashia Boland & Hadi Charkhgard & Martin Savelsbergh, 2015. "A Criterion Space Search Algorithm for Biobjective Integer Programming: The Balanced Box Method," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 735-754, November.
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