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An Analysis of Gene Regulatory Network Topology Using Results of DNA Microchip Experiments

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Advances in Intelligent Systems and Computing V (CSIT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1293))

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Abstract

In this paper, we have presented the research results concerning analysis of gene regulatory network structure based on Cytoscape software tools using graph theory. The gene regulatory network reconstructed using gene expression profiles of patients which were investigated on Alzheimer disease was used as an experimental one during the simulation process. The analysis process was performed by calculation of both the simple and distributed topological parameters which determine the network structure and the character of the appropriate simple parameters dependence between each other respectively. The objective of the research is development of the technique of gene regulatory network topology optimization based on complex use of the network topological parameters. The methods based on graph theory and multicriterial optimization technique were used for data processing during the simulation process. The charts of both the simple and distributed topological parameters were created as the result of the simulation. The final decision concerning choice of the optimal network topology was done based on complex topological index which was calculated using Harrington desirability function. The simulation results analysis has allowed determining the optimal strategy of gene regulatory networks reconstruction based on gene expression profiles with following development of effective technique of reconstructed networks validation based on analysis of appropriate networks topological parameters.

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References

  1. Delgado, F.M., Gómez-Vela, F.: Computational methods for Gene Regulatory Networks reconstruction and analysis: a review. Artif. Intell. Med. 95, 133–145 (2019). https://doi.org/10.1016/j.artmed.2018.10.006

    Article  Google Scholar 

  2. Van Der Wijst, M.G.P., De Vries, D.H., Brugge, H., Westra, H.-J., Franke, L.: An integrative approach for building personalized gene regulatory networks for precision medicine. Genome Med. 10(1), art. no. 96 (2018) https://doi.org/10.1186/s13073-018-0608-4

  3. Zheng, G., Huang, T.: The reconstruction and analysis of gene regulatory networks. In: Huang, T. (ed.) Computational Systems Biology. MMB, vol. 1754, pp. 137–154. Springer, New York (2018). https://doi.org/10.1007/978-1-4939-7717-8_8

    Chapter  Google Scholar 

  4. Thompson, D., Regev, A., Roy, S.: Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Ann. Rev. Cell Dev. Biol. 31, 399–428 (2015). https://doi.org/10.1146/annurev-cellbio-100913-012908

    Article  Google Scholar 

  5. Liu, X., Tian, R., Gao, J., Liu, D., Wang, Z.: Multiplexed detection of microRNAs by a competitive DNA microarray-based resonance light scattering assay. Analyst 142(23), 4529–4535 (2017). https://doi.org/10.1039/c7an01152k

    Article  Google Scholar 

  6. Shen, L., Jiang, H., He, M., Liu, G.: Collaborative representation-based classification of microarray gene expression data. PLoS ONE, 12(12), art. no. e0189533 (2017) https://doi.org/10.1371/journal.pone.0189533

  7. Gan, Y., Li, N., Zou, G., Xin, Y., Guan, J.: Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method. BMC Medical Genom. 11, art. no. 117 (2018). https://doi.org/10.1186/s12920-018-0433-z

  8. Zhang, X., Li, C., at al.: Expression profiles of MicroRNAs from multiple lumbar spine in sheep. Gene, 678, 105–114 (2018). https://doi.org/10.1016/j.gene.2018.08.020

  9. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., Morishima, K.: KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45(D1), D353–D361 (2017). https://doi.org/10.1093/nar/gkw1092

    Article  Google Scholar 

  10. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., Tanabe, M.: KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44(D1), D457–D462 (2016). https://doi.org/10.1093/nar/gkv1070

    Article  Google Scholar 

  11. Liang, W.S., Dunckley, T., Beach, at al.: Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol. Genom. 28(3), 311–322 (2007) https://doi.org/10.1152/physiolgenomics.00208.2006

  12. Liang, W.S., Reiman, E.M., Valla, J., at al.: Alzheimer’s disease is associated with reduced expression of energy metabolism genes in posterior cingulate neurons. In: Proceedings of the National Academy of Sciences of the United States of America, 105(11), 4441–4446 (2008). https://doi.org/10.1073/pnas.0709259105

  13. Babichev, S., Barilla, J., Fišer, J., Škvor, J.: A hybrid model of gene expression profiles reducing based on the complex use of fuzzy inference system and clustering quality criteria. In: 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). Atlantis Press (2019/08). https://doi.org/10.2991/eusflat-19.2019.20

  14. Liang, K.-C., Wang, X.: Gene regulatory network reconstruction using conditional mutual information. EURASIP J. Bioinform. Syst. Biol. 2008(1), 1–14 (2008). https://doi.org/10.1155/2008/253894

    Article  Google Scholar 

  15. Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T.: Cytoscape: a software Environment for integrated models of biomolecular interaction networks. Genome Res. 13(11), 2498–2504 (2003). https://doi.org/10.1101/gr.1239303

    Article  Google Scholar 

  16. De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 67–103 (2002). https://doi.org/10.1089/10665270252833208

    Article  MathSciNet  Google Scholar 

  17. Saint-Antoine, M.M., Singh, A.: Network inference in systems biology: recent developments, challenges, and applications. Current Opinion Biotechnol. 63, 89–98 (2020). https://doi.org/10.1016/j.copbio.2019.12.002

    Article  Google Scholar 

  18. Seldin, M., Yang, X., Lusis, A.J.: Systems genetics applications in metabolism research. Nat. Metabolism 1(11), 1038–1050 (2019). https://doi.org/10.1038/s42255-019-0132-x

    Article  Google Scholar 

  19. Groß, A., Kracher, B., at al.: Representing dynamic biological networks with multi-scale probabilistic models. Commun. Biol. 2(1), art. no. 21 (2019) https://doi.org/10.1038/s42003-018-0268-3

  20. Haliki, E., Alpagut Keskin, N., Masalci, O.: Boolean gene regulatory network model of centromere function in Saccharomyces cerevisiae. J. Biol. Phys. 45(3), 235–251 (2019). https://doi.org/10.1007/s10867-019-09526-4

    Article  Google Scholar 

  21. Shmulevich, I., Dougherty, E.R., Kim, S., Zhang, W.: Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18(2), 261–274 (2002). https://doi.org/10.1093/bioinformatics/18.2.261

    Article  Google Scholar 

  22. Trairatphisan, P., Mizera, A., Pang, J., Tantar, A.A., Schneider, J., Sauter, T.: Recent development and biomedical applications of probabilistic Boolean networks. Cell Commun. Signal. 11(1), art. no. 46 (2013) https://doi.org/10.1186/1478-811x-11-46

  23. Koul, N., Manvi, S.K.S.: Inference of gene regulatory networks for prostate cancer using Bayesian networks with feedback and feed forward loops. Int. J. Eng. Adv. Technol. 8(5 SpecialIssue), 137–147 (2019)

    Google Scholar 

  24. Ni, Y., Marchetti, G.M., Baladandayuthapani, V., Stingo, F.C.: Bayesian approaches for large biological networks. In: Mitra, R., Müller, P. (eds.) Nonparametric Bayesian Inference in Biostatistics. FPSS, pp. 153–173. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19518-6_8

    Chapter  MATH  Google Scholar 

  25. Babichev, S., Korobchynskyi, M., et al.: Development of a technique for the reconstruction and validation of gene network models based on gene expression profiles. Eastern-Euro. J. Enterprise Technol. 1(4–91), 19–32 (2018). https://doi.org/10.15587/1729-4061.2018.123634

  26. Babichev, S., Lytvynenko, V., Skvor, J., Korobchynskyi, M., Voronenko, M.: Information technology of gene expression profiles processing for purpose of gene regulatory networks reconstruction. In: Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018, art. no. 8478452, pp. 336–341 (2018). https://doi.org/10.1109/dsmp.2018.8478452

  27. Babichev, S.A., Kornelyuk, A.I., Lytvynenko, V.I., Osypenko, V.V.: Computational analysis of microarray gene expression profiles of lung cancer. Biopolym. Cell 32(1), 70–79 (2016). https://doi.org/10.7124/bc.00090F

    Article  Google Scholar 

  28. Rzheuskyi, A., Kutyuk, O., Vysotska, V., Burov, Y., Lytvyn, V., Chyrun, L.: The architecture of distant competencies analyzing system for IT recruitment. In: IEEE 2019 14th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2019 - Proceedings, 3, art. no. 8929762, pp. 254–261 (2019). https://doi.org/10.1109/stc-csit.2019.8929762

  29. Chyrun, L., Leshchynskyy, E., Lytvyn, V., Rzheuskyi, A., Vysotska, V., Borzov, Y.: Intellectual analysis of making decisions tree in information systems of screening observation for immunological patients. CEUR Workshop Proc. 2488, 281–296 (2019)

    Google Scholar 

  30. Tkachenko, R., Izonin, I., Vitynskyi, P., Lotoshynska, N., Pavlyuk, O.: Development of the non-iterative supervised learning predictor based on the ITO decomposition and SGTM neural-like structure for managing medical insurance costs. Data, 3(4), art. no. 46 (2018). https://doi.org/10.3390/data3040046

  31. Hu, Z., Bodyanskiy, Y., Tyshchenko, O., Samitova, V.: Possibilistic fuzzy clustering for categorical data arrays based on frequency prototypes and dissimilarity measures. Int. J. Intell. Syst. Appl. 9(5), 55–61 (2017). https://doi.org/10.5815/ijisa.2017.05.07

    Article  Google Scholar 

  32. Huang, L.-C., Wu, P.-A., Lin, S.-Z., Pang, C.-Y., Chen, S.-Y.: Graph theory and network topological metrics may be the potential biomarker in Parkinson’s disease. J. Clin. Neurosci. 68, 235–242 (2019). https://doi.org/10.1016/j.jocn.2019.07.082

    Article  Google Scholar 

  33. Assenov, Y., Ramírez, F., Schelhorn, S.-E.Sven-Eric, Lengauer, T., Albrecht, M.: Computing topological parameters of biological networks. Bioinformatics, 24(2), 282–284 (2008). https://doi.org/10.1093/bioinformatics/btm554

  34. Harrington, J.: The desirability function. Ind. Qual. Control 21(10), 494–498 (1965)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Jan Evangelista Purkyně University in Ústí Nad Labem, Pasteurova, 3544/1, 40096, Ústí Nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson State University, University Street, 27, Kherson, 73003, Ukraine

    Sergii Babichev

  3. Ukrainian Academy of Printing, Pid Holoskom, 19, Lviv, 79000, Ukraine

    Orest Khamula & Bohdan Durnyak

  4. Kharkiv National University of Radio Electronics, Nauky Ave, 14, Kharkiv, 61000, Ukraine

    Iryna Perova

Authors
  1. Sergii Babichev
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  2. Orest Khamula
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  3. Iryna Perova
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  4. Bohdan Durnyak
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Corresponding author

Correspondence to Sergii Babichev .

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Babichev, S., Khamula, O., Perova, I., Durnyak, B. (2021). An Analysis of Gene Regulatory Network Topology Using Results of DNA Microchip Experiments. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_9

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