Skip to main content

A Pycellerator Tutorial

  • Protocol
  • First Online:
  • 601 Accesses

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1945))

Abstract

We present a tutorial on using Pycellerator for biomolecular simulations. Models are described in human readable (and editable) text files (UTF8 or ASCII) containing collections of reactions, assignments, initial conditions, function definitions, and rate constants. These models are then converted into a Python program that can optionally solve the system, e.g., as a system of differential equations using ODEINT, or be run by another program. The input language implements an extended version of the Cellerator arrow notation, including mass action, Hill functions, S-Systems, MWC, and reactions with user-defined kinetic laws. Simple flux balance analysis is also implemented. We will demonstrate the implementation and analysis of progressively more complex models, starting from simple mass action through indexed cascades. Pycellerator can be used as a library that is integrated into other programs, run as a command line program, or in iPython notebooks. It is implemented in Python 2.7 and available under an open source GPL license.

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

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Shapiro BE, Mjolsess E (2016) Pycellerator: an arrow-based reaction-like modelling language for biological simulations. Bioinformatics 32(4):629–631. https://doi.org/10.1093/bioinformatics/btv596

    Article  CAS  Google Scholar 

  2. Shapiro BE, Levchenko A, Meyerowitz EM, Wold BJ, Mjolsness ED (2003) Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations. Bioinformatics 19:677–678. https://doi.org/10.1093/bioinformatics/btg042

    Article  CAS  Google Scholar 

  3. Hucka M, Finney A, Sauro HM et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:513–523. https://doi.org/10.1093/bioinformatics/btg015

    Article  Google Scholar 

  4. Jones E, Oliphant E, Peterson P et al (2001) SciPy: open source scientific tools for Python. http://www.scipy.org/. Accessed 1 Aug 2016

  5. van der Walt S, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13:22–30. https://doi.org/10.1109/MCSE.2011.37

    Article  Google Scholar 

  6. Waage P, Guldherg CM (1864) Studies in affinity [in Swedish, as Forhandlinger i Videnskabs Selskabet i Christiania, 37], (translated by H.I. Abrash, reprinted in Journal of Chemical Education, 63(12):1044–1047 (1966)

    Google Scholar 

  7. Field RJ, Noyes RM (1974) Oscillations in chemical systems. IV. Limit cycle behavior in a model of a real chemical reaction. J Chem Phys 60:1877–1884. https://doi.org/10.1064/1.1681288

    CAS  Google Scholar 

  8. Henri V (1903) Lois Générales de l’action des Distases. Hermann, Paris

    Google Scholar 

  9. Michaelis L, Menten ML (1913) Die Kinetik der Invertinwirkung. Biochem Z 49:333–369

    CAS  Google Scholar 

  10. Briggs GE, Haldane JBS (1925) A note on the kinetics of enzyme action. Biochem J 19(2):338–339. https://doi.org/10.1042/bj0190338

    Article  CAS  Google Scholar 

  11. Hill AV (1910) The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curve. Proc Physiol Soc 40(Suppl):4–7. https://doi.org/10.1113/jphysiol.1910.sp001386

    Google Scholar 

  12. Mjolsness E, Sharp DH, Reinitz J (1991) A connectionist model of development. J Theor Biol 152:429–453. https://doi.org/10.1016/S0022-5193(05)80391-1

  13. Murphy KP (2012) Machine learning, a probabilistic perspective. MIT Press, Cambridge

    Google Scholar 

  14. Monod J, Wyman J, Changeux JP (1965) On the nature of allosteric transitions: a plausible model. J Mol Biol 12:88–118. https://doi.org/10.1016/S0022-2836(65)80285-6

  15. Najdi T, Yang C-R, Shapiro BE, Hatfield W, Mjolsness E (2006) Application of a generalized MWC model for the mathematical simulation of metabolic pathways regulated by allosteric enzymes. J Bioinform Comput Biol 4(2):335–355. https://doi.org/10.1142/S0219720006001862

    Article  CAS  Google Scholar 

  16. Mjolsness E (2000) Trainable gene regulation networks with applications to Drosophila pattern formation. In: Bower JM, Bolouri H (eds) Computational models of genetic and biochemical networks. MIT Press, Cambridge

    Google Scholar 

  17. Shapiro BE, Mjolsness ED (2001) Developmental simulations with Cellerator. Paper presented at Second International Conference on Systems Biology, Pasadena, 4–7 Nov 2001

    Google Scholar 

  18. Huang CY, Ferrell JE Jr (1996) Ultrasensitivity in the mitogen-activated protein kinase cascade. Proc Natl Acad Sci USA 93(19):10078–10083

    Article  CAS  Google Scholar 

  19. Kholodenko BN (2000) Negative feedback and ultrasensitivity can bring about oscillations in the mitogen-activated protein kinase cascades. Eur J Biochem 267(6):1583–1588 https://doi.org/10.1046/j.1432-1327.2000.01197.x

    Article  CAS  Google Scholar 

  20. Mitchell S, O’Sulivan M, Dunning I (2011) PuLP: a linear programming toolkit for Python. http://www.optimization-online.org/DB_FILE/2011/09/3178.pdf. Accessed 31 July 2016

  21. Goldbeter A (1991) A minimal cascade model for the mitotic oscillator involving cyclin and cdc2 kinase. Proc Natl Acad Sci USA 88:9107–1101. https://doi.org/10.1073/pnas.88.20.9107

    Article  CAS  Google Scholar 

  22. Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. Proc R Soc A 115(772):700–721. https://doi.org/10.1098/rspa.1927.0118

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bruce E. Shapiro .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Shapiro, B.E., Mjolsness, E. (2019). A Pycellerator Tutorial. In: Hlavacek, W. (eds) Modeling Biomolecular Site Dynamics. Methods in Molecular Biology, vol 1945. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9102-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9102-0_1

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9100-6

  • Online ISBN: 978-1-4939-9102-0

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics