Skip to main content
SpringerLink
Log in

Application of Computer Technologies to the Study of Bas Properties in Biological Systems

  • Conference paper
  • First Online:
Data Science and Algorithms in Systems (CoMeSySo 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 597))

Included in the following conference series:

Abstract

This research focused on biological systems, specifically the life cycle of Caenorhabditis elegans larvae. The effect of biologically active plant-derived substances (24 samples of medicinal plant components) on larval growth rate was studied. The ImageJ program was used to process images of the biological system (larvae) in combination with pattern recognition algorithms and machine learning. The use of correlation analysis allowed optimizing the number of studied dependent variables for consideration. The Kruskal-Wallis and median tests were used for samples that did not fit the normal distribution. The analysis of variance revealed that some variables were significantly dependent on the induction time, while others were dependent on the samples of biologically active substances (BAS) components. When comparing the growth rate of larvae to the control, three BAS samples showed a significant difference; for remaining samples no significant influence of BAS components at a fixed concentration on growth dynamics was found. Using the information on the transition of larvae from one stage to the next with the addition of the BAS component, the following samples with significant biological activity were identified: naringenin from Medicágo satíva, baicalin from Scutellaria baicalensis, ononin from Trifolium pratense, 18-genistein from Trifolium pratense, apigenin from Achillea millefolium, rutin from Filipéndula ulmária, ursolic acid from Thymus vulgaris, myricetin from Hedysarum neglectum, and kaempferol from Panax ginseng. The results of the qualitative and quantitative characteristics of the impact on the biological system allowed the components of biologically active substances with pronounced biological activity to be selected for further detailed study.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Husson, A., Krivine, J.: A Tractable logic for molecular biology. Electron. Proc. Theor. Comput. Sci. 306, 101 (2019). https://doi.org/10.4204/EPTCS.306.17

    Article  MathSciNet  MATH  Google Scholar 

  2. Torres, J.P., Schmidt, E.W.: The biosynthetic diversity of the animal world. J. Biol. Chem. 294(46), 17684–17692 (2019)

    Article  Google Scholar 

  3. Kountz, D.J., Balskus, E.P.: Leveraging microbial genomes and genomic context for chemical discovery. Acc. Chem. Res. 54(13), 2788–2797 (2021). https://doi.org/10.1021/acs.accounts.1c00100

    Article  Google Scholar 

  4. Ganusov, V.V.: Strong inference in mathematical modeling: a method for robust science in the twenty-first century. Front. Microbiol. 7, 1131 (2016). https://doi.org/10.3389/fmicb.2016.01131

    Article  Google Scholar 

  5. Despeyroux, J.: (Mathematical) logic for systems biology (Invited Paper). Comput. Methods Syst. Biol. 9859, 3–12 (2016)

    Google Scholar 

  6. McQuin, C., Goodman, A., Chernyshev, V., Kamentsky, L., Cimini, B.A., Karhohs, K.W., Doan, M., Ding, L., Rafelski, S.M., Thirstrup, D., Wiegraebe, W., Singh, S., Becker, T., Caicedo, J.C., Carpenter, A.E.: CellProfiler 3.0: Next-generation image processing for biology. PLoS Biol. 16(7), e2005970 (2018). https://doi.org/10.1371/journal.pbio.2005970

  7. Wählby, C., Kamentsky, L., Liu, Z.H., Riklin-Raviv, T., Conery, A.L., O’Rourke, E.J., Sokolnicki, K.L., Visvikis, O., Ljosa, V., Irazoqui, J.E., Golland, P., Ruvkun, G., Ausubel, F.M., Carpenter, A.E.: An image analysis toolbox for high-throughput C. elegans assays. Nat. Methods 9(7), 714–716 (2012). https://doi.org/10.1038/nmeth.1984

Download references

Acknowledgments

This research was funded by the Ministry of Science and Higher Education of the Russian Federation, project number FZSR-2020-0006.

Author information

Authors and Affiliations

  1. Kemerovo State University, Krasnaya Street 6, Kemerovo, 650043, Russia

    Svetlana Ivanova, Lyubov Dyshlyuk, Anastasya Dmitrieva, Anna Loseva & Valery Pavsky

  2. Moscow State University of Food Production, Volokolamsk Highway 11, Moscow, 125080, Russia

    Mohammed El Amine Khelef

Authors
  1. Svetlana Ivanova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lyubov Dyshlyuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anastasya Dmitrieva
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anna Loseva
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammed El Amine Khelef
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Valery Pavsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Svetlana Ivanova .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Petr Silhavy

  3. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Zdenka Prokopova

Appendices

Appendix 1. Results of Statistical Analysis

See Fig. 3.

Fig. 3.
figure 3figure 3

Examining the normality of the distribution of dependent variable sample values: (a) h; (b) l; (c) S; (d) L1-L4.

Full size image

Appendix 2. Results of Statistical Analysis Using the Kruskal-Wallis Test

See Tables 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 and 18

Table 3. Sample analysis (dependence of the response function value h of the larvae width on the time of induction).
Full size table
Table 4. The median test results of sample analysis (dependence of the response function value h of the larvae width on the induction time).
Full size table
Table 5. Analysis of samples (dependence of the response function value l of the larvae length on the induction time) using the Kruskal-Wallis test.
Full size table
Table 6. The analysis of samples (dependence of the response function value l of the larvae length on the induction time) using the median test.
Full size table
Table 7. The analysis of samples (dependence of the response function S of the larvae area on the induction time) using the Kruskal-Wallis test.
Full size table
Table 8. The analysis of samples (dependence of the response function S of the larvae area on the induction time) using the median test.
Full size table
Table 9. The analysis of samples (dependence of the transition function value of larvae from L1 stage to L4 stage on the induction time) using the Kruskal-Wallis test.
Full size table
Table 10. The analysis of samples (dependence of the transition function value of larvae from L1 stage to L4 stage on the induction time) using the median test.
Full size table
Table 11. The analysis of samples (dependence of the response function value of the larvae width on the sample of BAS components) using the Kruskal-Wallis test.
Full size table
Table 12. The analysis of samples (dependence of the response function value of the larvae width on the sample of BAS components) using the median test.
Full size table
Table 13. The analysis of samples (dependence of the response function value of the larvae length on the sample of BAS components) using the Kruskal-Wallis test.
Full size table
Table 14. The analysis of samples (dependence of the response function value of the larvae length on the sample of BAS components) using the median test.
Full size table
Table 15. The analysis of samples (dependence of the response function value of the larvae area on the sample of BAS components) using the Kruskal-Wallis test.
Full size table
Table 16. The analysis of samples (dependence of the response function value of the larvae area on the sample of BAS components) using the median test.
Full size table
Table 17. The analysis of samples (dependence of the response function of the larvae transition from L1 stage to L4 stage on the sample of BAS components) using the Kruskal-Wallis test.
Full size table
Table 18. The analysis of samples (dependence of the response function of the larvae transition from L1 stage to L4 stage on the sample of BAS components) using a median test.
Full size table

Appendix 3. Analysis of Variance (ANOVA) of Samples

See Tables 19, 20, 21 and 22

Table 19. Analysis of variance (ANOVA) of samples describing the dependence of the value of the larval length response function on the induction duration.
Full size table
Table 20. Analysis of variance (ANOVA) of samples describing the dependence of the value of the response function of the growth rate of larvae on the induction duration
Full size table
Table 21. Analysis of variance (ANOVA) of samples describing the dependence of the value of the larval length response function on samples of BAS components.
Full size table
Table 22. Analysis of variance (ANOVA) of samples describing the dependence of the value of the larval growth rate response function on samples of BAS components.
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ivanova, S., Dyshlyuk, L., Dmitrieva, A., Loseva, A., El Amine Khelef, M., Pavsky, V. (2023). Application of Computer Technologies to the Study of Bas Properties in Biological Systems. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Data Science and Algorithms in Systems. CoMeSySo 2022. Lecture Notes in Networks and Systems, vol 597. Springer, Cham. https://doi.org/10.1007/978-3-031-21438-7_32

Download citation

Publish with us

Policies and ethics

Search

Navigation