Bio-Stark: A Tool for the Time-Point Robustness Analysis of Biological Systems | SpringerLink
Skip to main content
Springer Nature Link
Log in

Bio-Stark: A Tool for the Time-Point Robustness Analysis of Biological Systems

  • Conference paper
  • First Online:
Computational Methods in Systems Biology (CMSB 2024)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 14971))

Included in the following conference series:

  • 263 Accesses

Abstract

We present Bio-Stark, an extension of Stark for the simulation and analysis of biological systems. Specifically, to simulate the stochastic, dynamical, behaviour of these systems, Bio-Stark exploits the core simulation model of Stark, the evolution sequence model, and it extends it by refining the discrete step modelling into a time point modelling. We show how Bio-Stark allows us to verify robustness properties in systems biology, by capturing the effects of (unpredictable) perturbations on species in biochemical networks, as well as on the oscillatory behaviour of gene regulatory networks.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 22879
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 28599
Price includes VAT (Japan)
  • 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

Notes

  1. 1.

    Code available at https://github.com/stark-tool/Bio-STARK/tree/Tony/examples/repressilator.

  2. 2.

    Code available at https://github.com/stark-tool/Bio-STARK/tree/Tony/examples/Isocitrate.

References

  1. Ballarini, P., Bentriou, M., Cournède, P.: A formal approach for tuning stochastic oscillators. In: Pang, J., Niehren, J. (eds.) CMSB 2023. LNCS, vol. 14137, pp. 1–17. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42697-1_1

    Chapter  Google Scholar 

  2. Barkai, N., Leibler, S.: Robustness in simple biochemical networks. Nature 387, 913–917 (1997). https://doi.org/10.1038/43199

    Article  Google Scholar 

  3. Casal, J.J., Yanossky, M.J.: Regulation of gene expression by light. Int. J. Dev. Biol. 49, 501–511 (2005). https://doi.org/10.1387/ijdb.051973jc

    Article  Google Scholar 

  4. Castiglioni, V., Lanotte, R., Loreti, M., Tini, S.: Evaluating the effectiveness of digital twins through statistical model checking with feedback and perturbations. In: Haxthausen, A.E., Serwe, W. (eds.) FMICS 2024. LNCS, vol. 14952, pp. 21–39. Springer, Cham (2024). https://doi.org/10.1007/978-3-031-68150-9_2

    Chapter  Google Scholar 

  5. Castiglioni, V., Loreti, M., Tini, S.: Measuring adaptability and reliability of large scale systems. In: Margaria, T., Steffen, B. (eds.) ISoLA 2020, Part II. LNCS, vol. 12477, pp. 380–396. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61470-6_23

    Chapter  Google Scholar 

  6. Castiglioni, V., Loreti, M., Tini, S.: How adaptive and reliable is your program? In: Peters, K., Willemse, T.A.C. (eds.) FORTE 2021. LNCS, vol. 12719, pp. 60–79. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-78089-0_4

    Chapter  Google Scholar 

  7. Castiglioni, V., Loreti, M., Tini, S.: RobTL: a temporal logic for the robustness of cyber-physical systems. CoRR abs/2212.11158 (2022). https://doi.org/10.48550/arXiv.2212.11158

  8. Castiglioni, V., Loreti, M., Tini, S.: A framework to measure the robustness of programs in the unpredictable environment. Log. Methods Comput. Sci. 19(3) (2023). https://doi.org/10.46298/LMCS-19(3:2)2023

  9. Castiglioni, V., Loreti, M., Tini, S.: STARK: a software tool for the analysis of robustness in the unknown environment. In: Jongmans, S.S., Lopes, A. (eds.) COORDINATION 2023. LNCS, vol. 13908, pp. 115–132. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-35361-1_6

    Chapter  Google Scholar 

  10. Castiglioni, V., Loreti, M., Tini, S.: RobTL: robustness temporal logic for CPS. In: Proceedings of CONCUR 2024. LIPIcs, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2024, to appear)

    Google Scholar 

  11. Castiglioni, V., Loreti, M., Tini, S.: STARK: a tool for the analysis of CPSs robustness. Sci. Comput. Program. 236, 103134 (2024). https://doi.org/10.1016/j.scico.2024.103134

    Article  Google Scholar 

  12. Dexter, J.P., Gunawardena, J.: Dimerization and bifunctionality confer robustness to the Isocitrate Dehydrogenase regulatory system in Escherichia coli. J. Biol. Chem. 288(8), 5770–5778 (2013). https://doi.org/10.1074/jbc.M112.339226

    Article  Google Scholar 

  13. Elowitz, M.B., Leibler, S.: A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000). https://doi.org/10.1038/35002125

    Article  Google Scholar 

  14. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81(25), 2340–2361 (1977)

    Article  Google Scholar 

  15. Herbach, U.: Harissa: stochastic simulation and inference of gene regulatory networks based on transcriptional bursting. In: Pang, J., Niehren, J. (eds.) CMSB 2023. LNCS, vol. 14137, pp. 97–105. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-42697-1_7

    Chapter  Google Scholar 

  16. Herbach, U., Bonnaffoux, A., Espinasse, T., Gandrillon, O.: Inferring gene regulatory networks from single-cell data: a mechanistic approach. BMC Syst. Biol. 11, 335–338 (2017). https://doi.org/10.1186/s12918-017-0487-0

    Article  Google Scholar 

  17. Jia, X., He, X., Huang, C., Li, J., Dong, Z., Liu, K.: Protein translation: biological processes and therapeutic strategies for human diseases. Signal Transduct. Target. Ther. 9(44), 2786–2791 (2024). https://doi.org/10.1038/s41392-024-01749-9

    Article  Google Scholar 

  18. Kim, J., Enciso, G.: Absolutely robust controllers for chemical reaction networks. J. R. Soc. Interface 17, 20200031 (2020). https://doi.org/10.1098/rsif.2020.0031

    Article  Google Scholar 

  19. Kitano, H.: Towards a theory of biological robustness. Mol. Syst. Biol. 3(1), 137 (2007). https://doi.org/10.1038/msb4100179

    Article  Google Scholar 

  20. Lanotte, R., Manicardi, D., Tini, S.: Step-by-step robustness for biochemical networks. In: Proceedings of ICTCS 2023. CEUR Workshop Proceedings, vol. 3587, pp. 299–313. CEUR-WS.org (2023). https://ceur-ws.org/Vol-3587/2906.pdf

  21. Nasti, L.: Verification of robustness property in chemical reaction networks. Ph.D. thesis, University of Pisa, Italy (2020). https://etd.adm.unipi.it/theses/available/etd-02172020-165444/

  22. Nasti, L., Gori, R., Milazzo, P.: Formalizing a notion of concentration robustness for biochemical networks. In: Mazzara, M., Ober, I., Salaün, G. (eds.) STAF 2018. LNCS, vol. 11176, pp. 81–97. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-04771-9_8

    Chapter  Google Scholar 

  23. Pernice, S., et al.: Computational modeling of the immune response in multiple sclerosis using epimod framework. BMC Bioinform. 21-S(17), 550 (2020). https://doi.org/10.1186/S12859-020-03823-9

  24. Petrillo, E., Herz, M., Barta, A., Kalyna, M., Kornblihtt, A.: Let there be light: regulation of gene expression in plants. RNA Biol. 11(10), 1215–1220 (2014). https://doi.org/10.4161/15476286.2014.972852

    Article  Google Scholar 

  25. Raun, W.R., et al.: Unpredictable nature of environment on nitrogen supply and demand. Agron. J. 111(6), 2786–2791 (2019). https://doi.org/10.2134/agronj2019.04.0291

    Article  Google Scholar 

  26. Rizk, A., Batt, G., Fages, F., Soliman, S.: A general computational method for robustness analysis with applications to synthetic gene networks. Bioinform. 25(12) (2009). https://doi.org/10.1093/bioinformatics/btp200

  27. Rizk, A., Batt, G., Fages, F., Soliman, S.: Continuous valuations of temporal logic specifications with applications to parameter optimization and robustness measures. Theor. Comput. Sci. 412(26), 2827–2839 (2011). https://doi.org/10.1016/j.tcs.2010.05.008

    Article  MathSciNet  Google Scholar 

  28. Shinar, G., Feinberg, M.: Structural sources of robustness in biochemical reaction networks. Science 327(5971), 1389–1391 (2010). https://doi.org/10.1126/science.1183372

    Article  Google Scholar 

  29. Shinar, G., Feinberg, M.: Design principles for robust biochemical reaction networks: what works, what cannot work, and what might almost work. Mathe. Biosci. 231(1), 39–48 (2011). https://doi.org/10.1016/j.mbs

    Article  MathSciNet  Google Scholar 

  30. Tunnacliffe, E., Chubb, J.R.: What is a transcriptional burst? Trends Genet. 36(4), 288–297 (2020). https://doi.org/10.1016/j.tig.2020.01.003

    Article  Google Scholar 

  31. Uri, A.: An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman and Hall/CRC (2006)

    Google Scholar 

  32. Vaserstein, L.N.: Markovian processes on countable space product describing large systems of automata. Probl. Peredachi Inf. 5(3), 64–72 (1969)

    Google Scholar 

Download references

Acknowledgements

This study received funding from the European Union - Next-GenerationEU - National Recovery and Resilience Plan (NRRP) - MISSION 4 COMPONENT 2, INVESTMENT N. 1.1, CALL PRIN 2022 D.D. 104 02-02-2022 - MEDICA Project, CUP N. J53D23007180006.

This publication is part of the project NODES which has received funding from the MUR - M4C2 1.5 of PNRR with grant agreement no. ECS00000036.

Author information

Authors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Valentina Castiglioni

  2. University of Camerino, Camerino, Italy

    Michele Loreti

  3. University of Insubria, Como, Italy

    Simone Tini

Authors
  1. Valentina Castiglioni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michele Loreti
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simone Tini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valentina Castiglioni .

Editor information

Editors and Affiliations

  1. University of Pisa, Pisa, Italy

    Roberta Gori

  2. Università di Pisa, Pisa, Italy

    Paolo Milazzo

  3. IMT School for Advanced Studies Lucca, Lucca, Italy

    Mirco Tribastone

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Castiglioni, V., Loreti, M., Tini, S. (2024). Bio-Stark: A Tool for the Time-Point Robustness Analysis of Biological Systems. In: Gori, R., Milazzo, P., Tribastone, M. (eds) Computational Methods in Systems Biology. CMSB 2024. Lecture Notes in Computer Science(), vol 14971. Springer, Cham. https://doi.org/10.1007/978-3-031-71671-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-71671-3_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-71670-6

  • Online ISBN: 978-3-031-71671-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation