Computational Platform FluorSimSudio for Processing the Kinetic Curves of Fluorescence Decay Using Simulation Modelling and Data Mining Algorithms

Herein, a computational platform FluorSimStudio was developed for processing fluorescence decay curves in molecular systems, which implements the concept of complex analysis of experimental information based on the simulation modelling and data mining methods. Data analysis includes partitioning the fluorescence decay curves into clusters according to the degree of likeness to some measure of similarity, finding the median cluster members (medoids), applying the data reduction method and visualizing the experimental data in a two-dimensional space. Analysis of the decay curves is carried out by the analytical or simulation models of optical processes occurring in molecular systems. The visualization of data clusters in the original and transformed time spaces is done with the aim of user interaction. A functional scheme of the platform is proposed, the choice of software for ensuring high computing performance is substantiated, a web application of the platform is implemented (https://dsa-cm.shinyapps.io/FluorSimStudio), and the results of a comparative analysis of the simulation algorithms are presented. The performance of the computational platform was confirmed by examples of the analysis of data sets representing systems of free fluoro-phores and in the presence of the Forster electronic excitation energy transfer process. The computational platform is an open system and allows permanent addition of complex analysis models, taking into account the development of new algorithms for modelling the energy transfer processes in molecular systems, studied with the use of time-resolved fluorescence spectroscopy systems.

1. R. R. Choubeh, L. Bar-Eya, Y. Paltiel, N. Keren, P. C. Struik, H. van Amerongen. Photosynth. Res., 143 (2020) 13—18

2. L. Michels, V. Gorelova, Y. Harnvanichvech, J. W. Borst, B. Albada, D. Weijers, J. Sprakel. Proc. Natl. Acad. Sci. USA, 117, N 30 (2020) 18110—18118

3. Fluorescence Spectroscopy and Microscopy: Methods and Protocols. Methods in Molecular Biology, Eds. Y. Engelborghs, A. J. W. G. Visser, Springer Science+Business Media, LLC (2014) 1076

4. J. T. Smith, R. Yao, N. Sinsuebphon, A. Rudkouskaya, N. Un, J. Mazurkiewicz, M. Barroso, P. Yan, X. Intes. Proc. Natl. Acad. Sci. USA, 116, N 48 (2019) 24019—24030

5. W. M. J. Franssen, F. J. Vergeldt, A. N. Bader, H. van Amerongen, C. Terenzi. J. Phys. Chem. Lett., 11, N 21 (2020) 9152—9158

6. M. M. Yatskou, V. V. Skakun, V. V. Apanasovich. J. Appl. Spectr., 87, N 2 (2020) 333—344

7. Н. Н. Яцков, В. В. Скакун, В. В. Гринев. Информатика, 16, № 4 (2019) 7—24

8. J. Demsar, T. Curk, A. Erjavec, C. Gorup, T. Hocevar, M. Milutinovic, M. Mozina, M. Polajnar, M. Toplak, A. Staric, M. Stajdohar, L. Umek, L. Zagar, J. Zbontar, M. Zitnik, B. Zupan. J. Machine Learn. Res., 14 (2013) 2349—2353

9. M. F. Hornick, E. Marcade, S. Venkayala. Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for Architecture, Design, and Implementation. Morgan Kaufmann Publishers Inc., San Francisco (2006)

10. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay. J. Machine Learn. Res., 12 (2011) 2825—2830

11. D. Schmidt, W.-C. Chen, M. A. Matheson, G. Ostrouchov. Big Data Res., 8 (2016) 1—11

12. T. Masters. Data Mining Algorithms in C++. Data Patterns and Algorithms for Modern Applications, Apress, eBook (2018)

13. J. M. Abui'n, N. Lopes, L. Ferreira, T. F. Pena, B. Schmidt. PLoS One, 15, N 10 (2020) e0239741, doi: 10.1371/journal.pone.0239741.

14. Apache Software Foundation. Apache Hadoop, http://hadoop.apache.org

15. R Core Team. R: A Language and Environment for Statistical Computing. Foundation for Statistical Computing, Vienna, Austria (2020), http://www.R-project.org

16. R. Gentleman, V. J. Carey, D. M. Bates. Genome Biology, 5, N 10 (2004) R80, doi: 10.1186/gb-2004-5-10-r80

17. H2O.ai. (2020) H2O: Scalable Machine Learning Platform. Version 3.30.0.6. https://github.com/h2oai/h2o-3

18. M. Zaharia, R. S. Xin, P. Wendell, T. Das, M. Armbrust, A. Dave, X. Meng, J. Rosen, S. Venkataraman, M. J. Franklin, A. Ghodsi, J. Gonzalez, S. Shenker, I. Stoica. Commun. ACM, 59, N 11 (2016) 56—65

19. T. Zhu, H. Chen, X. Yan, Z. Wu, X. Zhou, Q. Xiao, W. Ge, Q. Zhang, C. Xu, L. Xu, G. Ruan, Z. Xue, C. Yuan, G.-B. Chen, T. Guo. Bioinform. (2021) btaa1088, doi: 10.1093/bioinformatics/btaa1088

20. V. Yuan, D. Hui, Y. Yin, M. S. Penaherrera, A. G. Beristain, W. P. Robinson. BMC Genomic., 22, N 1 (2021), doi: 10.1186/s12864-020-07186-6

21. J. Lu, S. L. Salzberg. PLoS Comput Biol., 16, N 12 (2020) e1008439, doi: 10.1371/journal.pcbi.1008439

22. RStudio Team. RStudio: Integrated Development for R. RStudio, PBC, Boston (2020), http://www.rstudio.com

23. M. M. Yatskou. Computer Simulation of Energy Relaxation and Transport in Organized Porphyrin Systems, Wageningen (2001)

24. Н. Н. Яцков. Интеллектуальный анализ данных: пособие, Минск, БГУ (2014)

25. H. Shimodaira. Annal. Statist., 32 (2004) 2616—2641

26. T. Jolliffie. Principal Component Analysis, Springer, New York (2002)

27. J. A. Nelder, R. Mead. Comput. J., 8 (1965) 308—313

28. J. R. Lakowicz. Principles of Fluorescence Spectroscopy, Springer, New York (2006)