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Community Structure in Transcriptional Regulatory Networks of Yeast Species

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Complex Networks XIV (CompleNet 2023)

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Abstract

The genomic expression of living organisms is controlled by complex transcriptional regulation. Transcriptional regulatory networks, composed by associations between transcription factors and target genes, are responsible for representing and controlling this gene expression and regulate the response of an organism to environmental changes. In this paper, we extend the study of these systems by applying different community detection algorithms on closely-related yeast transcriptional regulation networks to characterize their topological structure and understand if these methods are able to capture meaningful functional clusters of genes. We start by evaluating the accuracy and efficiency of a large group of algorithms by applying them to benchmark networks with ground-truth communities. We then apply the methods that had the best performance to the yeast networks to analyze the quality of the resulting structures, and then, assess the quality of the retrieved modules from a biological point of view using available annotated species’ biological functions. Finally, we apply a multilayer community detection algorithm on multilayer networks, where each layer is an individual yeast network, and use available mappings between nodes of different species to successfully discover communities with genes that belong to different species but have similar biological functions. We conclude that the use of community detection algorithms to functionally characterize the modules of these networks might not be enough, suggesting the need of additional genetic information and possibly the use of alternative strategies to study these complex regulatory networks.

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Notes

  1. 1.

    http://interactome.dfci.harvard.edu/S_cerevisiae/.

  2. 2.

    https://string-db.org/.

References

  1. Babu, M.M., Luscombe, N.M., Aravind, L., Gerstein, M., Teichmann, S.A.: Structure and evolution of transcriptional regulatory networks. Current Opinion Struct. Biol. 14(3), 283–291 (2004)

    Article  Google Scholar 

  2. Barabási, A.-L.: Network science. Philosop. Trans. Royal Soc. A: Math. Phys. Eng. Sci. 371(1987), 20120375 (2013)

    Article  ADS  Google Scholar 

  3. Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)

    Article  MATH  Google Scholar 

  4. Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech: Theory Exp. 2005(09), P09008 (2005)

    Article  Google Scholar 

  5. De Domenico, M., Lancichinetti, A., Arenas, A., Rosvall, M.: Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys. Rev. X 5(1), 011027 (2015)

    Google Scholar 

  6. Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)

    Article  ADS  MathSciNet  MATH  Google Scholar 

  7. Hagberg, A., Swart, P., Chult, D.D.: Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States) (2008)

    Google Scholar 

  8. Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985)

    Google Scholar 

  9. Lancichinetti, A., Fortunato, S.: Consensus clustering in complex networks. Sci. Rep. 2(1), 1–7 (2012)

    Article  Google Scholar 

  10. Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)

    Article  ADS  Google Scholar 

  11. Lancichinetti, A., Radicchi, F., Ramasco, J.J.: Statistical significance of communities in networks. Phys. Rev. E 81(4), 046110 (2010)

    Article  ADS  Google Scholar 

  12. Latchman, D.S.: Transcription factors: an overview. Int. J. Biochem. Cell Biol. 29(12), 1305–1312 (1997)

    Article  Google Scholar 

  13. Lee, T.I., et al.: Transcriptional regulatory networks in saccharomyces cerevisiae. Science 298(5594), 799–804 (2002)

    Google Scholar 

  14. Li, P.-Z., Huang, L., Wang, C.-D., Lai, J.-H.: EdMot: an edge enhancement approach for motif-aware community detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 479–487 (2019)

    Google Scholar 

  15. Monteiro, P.T., et al.: YEASTRACT+: a portal for cross-species comparative genomics of transcription regulation in yeasts. Nucleic Acids Res. 48(D1), D642–D649 (2020)

    Article  Google Scholar 

  16. Monteiro, P.T., Pedreira, T., Galocha, M., Teixeira, M.C., Chaouiya, C.: Assessing regulatory features of the current transcriptional network of saccharomyces cerevisiae. Sci. Rep. 10(1), 1–11 (2020)

    Article  Google Scholar 

  17. Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Article  ADS  Google Scholar 

  18. Peixoto, T.P.: Descriptive vs. inferential community detection: pitfalls, myths and half-truths. arXiv preprint arXiv:2112.00183 (2021)

  19. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)

    Article  ADS  Google Scholar 

  20. Rossetti, G., Milli, L., Cazabet, R.: CDLIB: a python library to extract, compare and evaluate communities from complex networks. Appl. Netw. Sci. 4(1), 1–26 (2019)

    Article  Google Scholar 

  21. Rossetti, G., Pappalardo, L., Rinzivillo, S.: A novel approach to evaluate community detection algorithms on ground truth. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds.) Complex Networks VII. SCI, vol. 644, pp. 133–144. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30569-1_10

    Chapter  Google Scholar 

  22. Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)

    Article  ADS  Google Scholar 

  23. Traag, V.A., Waltman, L., Van Eck, N.J.: From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9(1), 1–12 (2019)

    Article  Google Scholar 

  24. Valdeolivas, A., et al.: Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics 35(3), 497–505 (2019)

    Article  Google Scholar 

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Acknowledgements

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references DSAIPA/AI/0033/2019 (Project LAIfeBlood), UIDB/50021/2020, UIDB/00408/2020 and UIDP/00408/2020 (INESC-ID and LASIGE multi-annual funding, respectively).

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Correspondence to Fábio Cruz .

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Cruz, F., Monteiro, P.T., Teixeira, A.S. (2023). Community Structure in Transcriptional Regulatory Networks of Yeast Species. In: Teixeira, A.S., Botta, F., Mendes, J.F., Menezes, R., Mangioni, G. (eds) Complex Networks XIV. CompleNet 2023. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-031-28276-8_4

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