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A comprehensive statistical study of metabolic and protein–protein interaction network properties

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  • Gamermann, D.
  • Triana-Dopico, J.
  • Jaime, R.

Abstract

Understanding the mathematical properties of graphs underlying biological systems could give hints on the evolutionary mechanisms behind these structures. In this article we perform a complete statistical analysis over thousands of graphs representing metabolic and protein–protein interaction (PPI) networks. First, we investigate the quality of fits obtained for the nodes degree distributions to power-law functions. This analysis suggests that a power-law distribution poorly describes the data except for the far right tail in the case of PPI networks. Next we obtain descriptive statistics for the main graph parameters and try to identify the properties that deviate from the expected values had the networks been built by randomly linking nodes with the same degree distribution. This survey identifies the properties of biological networks which are not solely the result of their degree distribution, but emerge from yet unidentified mechanisms other than those that drive these distributions. The findings suggest that, while PPI networks have properties that differ from their expected values in their randomized versions with great statistical significance, the differences for metabolic networks have a smaller statistical significance, though it is possible to identify some drift.

Suggested Citation

  • Gamermann, D. & Triana-Dopico, J. & Jaime, R., 2019. "A comprehensive statistical study of metabolic and protein–protein interaction network properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312798
    DOI: 10.1016/j.physa.2019.122204
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    References listed on IDEAS

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    1. Sudbrack, Vítor & Brunnet, Leonardo G. & de Almeida, Rita M.C. & Ferreira, Ricardo M. & Gamermann, Daniel, 2018. "Master equation for the degree distribution of a Duplication and Divergence network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 588-598.
    2. Einmahl, J. H.J. & Dekkers, A. L.M. & de Haan, L., 1989. "A moment estimator for the index of an extreme-value distribution," Other publications TiSEM 81970cb3-5b7a-4cad-9bf6-2, Tilburg University, School of Economics and Management.
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    Cited by:

    1. Gamermann, Daniel & Pellizzaro, José Antônio, 2022. "An algorithm for network community structure determination by surprise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    2. Radillo-Ochoa, Diego & Rodríguez-Hernández, Andrea & Terrero-Escalante, César A., 2023. "Bifurcation in cellular evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    3. Wang, Zhixiao & Rui, Xiaobin & Yuan, Guan & Cui, Jingjing & Hadzibeganovic, Tarik, 2021. "Endemic information-contagion outbreaks in complex networks with potential spreaders based recurrent-state transmission dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).

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