Skip to main content
SpringerLink
Log in

Quantifying Information Flow in Chemical Reaction Networks

  • Conference paper
  • First Online:
Algorithms for Computational Biology (AlCoB 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10252))

Included in the following conference series:

Abstract

We introduce an efficient algorithm for stochastic flux analysis of chemical reaction networks (CRN) that improves our previously published method for this task. The flux analysis algorithm extends Gillespie’s direct method, commonly used for stochastically simulating CRNs with respect to mass action kinetics. The extension to the direct method involves only book-keeping constructs, and does not require any labeling of network species. We provide implementations, and illustrate on examples that our algorithm for stochastic flux analysis provides a means for quantifying information flow in CRNs. We conclude our discussion with a case study of the biochemical mechanism of gemcitabine, a prodrug widely used for treating various carcinomas.

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 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

Notes

  1. 1.

    https://sites.google.com/site/ozankahramanogullari/software.

References

  1. Cardelli, L., Kwiatkowska, M., Laurenti, L.: Stochastic analysis of chemical reaction networks using linear noise approximation. Biosystems 149, 26–33 (2016)

    Article  MATH  Google Scholar 

  2. Erhard, F., Friedel, C.C., Zimmer, R.: FERN - a java framework for stochastic simulation and evaluation of reaction networks. BMC Bioinform. 9, 356 (2008)

    Article  Google Scholar 

  3. Fryer, R.A., Barlett, B., Galustian, C., Dalgleish, A.G.: Mechanisms underlying gemcitabine resistance in pancreatic cancer and sensitisation by the iMiD\(^{\rm TM}\) lenalidomide. Anticancer Res. 31(11), 3747–3756 (2011)

    Google Scholar 

  4. Funel, N., Giovannetti, E., Chiaro, M.D., Mey, V., Pollina, L.E., Nannizzi, S., Boggi, U., Ricciardi, S., Tacca, M.D., Bevilacqua, G., Mosca, F., Danesi, R., Campani, D.: Laser microdissection and primary cell cultures improve pharmacogenetic analysis in pancreatic adenocarcinoma. Lab Invest. 88(7), 773–784 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  6. Gillespie, D.T.: A rigorous derivation of the chemical master equation. Physica A 188, 404–425 (1992)

    Article  Google Scholar 

  7. Gillespie, D.T.: Approximate accelerated stochastic simulation of chemically reacting systems. J. Chem. Phys. 115(4), 1716 (2001)

    Article  Google Scholar 

  8. Kahramanoğulları, O.: On linear logic planning and concurrency. Inf. Comput. 207, 1229–1258 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  9. Kahramanoğulları, O., Fantaccini, G., Lecca, P., Morpurgo, D., Priami, C.: Algorithmic modeling quantifies the complementary contribution of metabolic inhibitions to gemcitabine efficacy. PLoS ONE 7(12), e50176 (2012)

    Article  Google Scholar 

  10. Kahramanoğulları, O., Lynch, J.: Stochastic flux analysis of chemical reaction networks. BMC Syst. Biol. 7, 133 (2013)

    Article  Google Scholar 

  11. Kuwahara, H., Mura, I.: An efficient and exact stochastic simulation method to analyze rare events in biochemical systems. J. Chem. Phys. 129(16), 10B619 (2008)

    Article  Google Scholar 

  12. Lotka, A.J.: Fluctuations in the abundance of a species considered mathematically. Nature 119, 12 (1927)

    Google Scholar 

  13. McQuarrie, D.A.: A rigorous derivation of the chemical master equation. J. Appl. Probab. 4, 413–478 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  14. Nielsen, M., Plotkin, G., Winskel, G.: Event structures and domains, part 1. Theor. Comput. Sci. 5(3), 223–256 (1981)

    MATH  Google Scholar 

  15. Okino, M.S., Mavrovouniotis, M.L.: Simplification of mathematical models of chemical reaction systems. Chem. Rev. 98(2), 391–408 (1998)

    Article  Google Scholar 

  16. Salis, H., Kaznessis, Y.N.: Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions. J. Chem. Phys. 122(5), 54103 (2005)

    Article  Google Scholar 

  17. Veltkamp, S.A., Beijnen, J.H., Schellens, J.H.: Prolonged versus standard gemcitabine infusion: translation of molecular pharmacology to new treatment strategy. Oncologist 13(3), 261–276 (2008)

    Article  Google Scholar 

  18. Volterra, V.: Fluctuations in the abundance of species considered mathematically. Nature 118, 558–560 (1926)

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This work has been partially funded by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 686585 - LIAR, Living Architecture.

Author information

Authors and Affiliations

  1. Department of Mathematics, University of Trento, Trento, Italy

    Ozan Kahramanoğulları

  2. The Micrososft Research - University of Trento Centre for Computational and Systems Biology, Rovereto, Italy

    Ozan Kahramanoğulları

Authors
  1. Ozan Kahramanoğulları
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Aveiro, Aveiro, Portugal

    Daniel Figueiredo

  2. Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  3. University of Aveiro, Aveiro, Portugal

    Diogo Pratas

  4. University of Extremadura, Caceres, Spain

    Miguel A. Vega-Rodríguez

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kahramanoğulları, O. (2017). Quantifying Information Flow in Chemical Reaction Networks. In: Figueiredo, D., Martín-Vide, C., Pratas, D., Vega-Rodríguez, M. (eds) Algorithms for Computational Biology. AlCoB 2017. Lecture Notes in Computer Science(), vol 10252. Springer, Cham. https://doi.org/10.1007/978-3-319-58163-7_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-58163-7_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58162-0

  • Online ISBN: 978-3-319-58163-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation