Skip to main content

StochNetV2: A Tool for Automated Deep Abstractions for Stochastic Reaction Networks

  • Conference paper
  • First Online:
Quantitative Evaluation of Systems (QEST 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12289))

Included in the following conference series:

  • 587 Accesses

Abstract

We present a toolbox for stochastic simulations with CRN models and their (automated) deep abstractions: a mixture density deep neural network trained on time-series data produced by the CRN. The optimal neural network architecture is learnt along with learning the transition kernel of the abstract process. Automated search of the architecture makes the method applicable directly to any given CRN, which is time-saving for deep learning experts and crucial for non-specialists. The tool was primarily designed to efficiently reproduce simulation traces of given complex stochastic reaction networks arising in systems biology research, possibly with multi-modal emergent phenotypes. It is at the same time applicable to any other application domain, where time-series measurements of a Markovian stochastic process are available by experiment or synthesised with simulation (e.g. are obtained from a rule-based description of the CRN).

TP’s research is supported by the Ministry of Science, Research and the Arts of the state of Baden-Württemberg, and the DFG Centre of Excellence 2117 ‘Centre for the Advanced Study of Collective Behaviour’ (ID: 422037984), DR’s research is supported by Young Scholar Fund (YSF), project no. \(P83943018 FP 430\_/18\) and by the ‘Centre for the Advanced Study of Collective Behaviour’. The authors would like to thank to Luca Bortolussi for inspiring discussions on the topic.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Similar content being viewed by others

Notes

  1. 1.

    The tool name makes it transparent that the tool was inspired by [2] called ‘StochNet’.

  2. 2.

    While we performed specific performance evaluation, e.g. in Fig. 2 and [8], a systematic scalability analysis is beyond the scope of this tool presentation.

References

  1. Bortolussi, L., Cairoli, F.: Bayesian abstraction of Markov population models. In: Parker, D., Wolf, V. (eds.) QEST 2019. LNCS, vol. 11785, pp. 259–276. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30281-8_15

    Chapter  Google Scholar 

  2. Bortolussi, L., Palmieri, L.: Deep abstractions of chemical reaction networks. In: Češka, M., Šafránek, D. (eds.) CMSB 2018. LNCS, vol. 11095, pp. 21–38. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99429-1_2

    Chapter  Google Scholar 

  3. Cai, H., Zhu, L., Han, S.: ProxylessNAS: direct neural architecture search on target task and hardware. CoRR abs/1812.00332 (2018). http://arxiv.org/abs/1812.00332

  4. Davis, C.N., Hollingsworth, T.D., Caudron, Q., Irvine, M.A.: The use of mixture density networks in the emulation of complex epidemiological individual-based models. PLoS Comput. Biol. 16(3), 1–16 (2020). https://doi.org/10.1371/journal.pcbi.1006869

    Article  Google Scholar 

  5. Feret, J., Henzinger, T., Koeppl, H., Petrov, T.: Lumpability abstractions of rule-based systems. Theoret. Comput. Sci. 431, 137–164 (2012)

    Article  MathSciNet  Google Scholar 

  6. Hajnal, M., Nouvian, M., Šafránek, D., Petrov, T.: Data-informed parameter synthesis for population Markov chains. In: Češka, M., Paoletti, N. (eds.) HSB 2019. LNCS, vol. 11705, pp. 147–164. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-28042-0_10

    Chapter  Google Scholar 

  7. Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=S1eYHoC5FX

  8. Petrov, T., Repin, D.: Automated deep abstractions for stochastic chemical reaction networks. arXiv preprint arXiv:2002.01889 (2020)

  9. Plesa, T., Erban, R., Othmer, H.G.: Noise-induced mixing and multimodality in reaction networks. Eur. J. Appl. Math. 30(5), 887–911 (2019)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tatjana Petrov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Repin, D., Phung, NH., Petrov, T. (2020). StochNetV2: A Tool for Automated Deep Abstractions for Stochastic Reaction Networks. In: Gribaudo, M., Jansen, D.N., Remke, A. (eds) Quantitative Evaluation of Systems. QEST 2020. Lecture Notes in Computer Science(), vol 12289. Springer, Cham. https://doi.org/10.1007/978-3-030-59854-9_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59854-9_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59853-2

  • Online ISBN: 978-3-030-59854-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics