Software:PySCeS

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PySCeS
Initial releaseFebruary 15, 2005; 19 years ago (2005-02-15)
Stable release
1.0.2 / May 1, 2022; 2 years ago (2022-05-01)
Written inPython,C++,C,FORTRAN
Operating systemLinux, macOS and Microsoft Windows
PlatformPython
LicenseBSD License
Websitepysces.sourceforge.net

PySCeS

PySCeS[1] is a Python-based open-source simulator for cellular systems. Originally developed by Brett Olivier in the early 2000s, the software has continued to be enhanced[2]. PySCeS was the first biochemical simulation software package written in Python. As well as being able to run time-dependent simulations, PySCeS also has extensive support for metabolic control analysis. The software runs on all major platforms, Windows, Mac OS, and Linux.

Capabilities

  • Time-course simulation using the LSODA solver of ordinary differential equations[3], which can report on the system's variable concentrations and reaction rates over time.
  • Steady-state calculations using non-linear solvers, such as HYBRD[4] and NLEQ2[5]
  • Metabolic control analysis can be carried out on the system, including calculating the |elasticities towards the variable metabolites by algebraic or numerical differentiation of the rate equations, as well as the flux and concentration control coefficients by means of matrix inversion[6] (Hofmeyr, 2001). PySCeS will also compute the structural matrices (e.g. K- and L-matrices) of a stoichiometric model[7].
  • The stability of a system can be investigated by way of the system eigenvalues. Continuation techniques are provided to trace stable and unable steady-states. PySCeS provides an interface to the PITCON algorithm for this purpose[8].
  • Data and results can be plotted via the SciPy GnuPlot interface, saved in text, LaTeX or web-ready HTML format.
  • PySCeS supports the import and export of standard SBML.

Applications

PySCeS has been widely used in the systems biology community for doing research in systems biology modeling:

  • Pillay et al.[9] used PySCeS to build a model of redox signaling.
  • Kerkhoven et al. used PySCeS to study the handling of uncertainty in dynamic models of Trypanosoma brucei.

As of Oct 2022, the original PySCeS publication has received 226 citations, according to Google Scholar.

Notability

  • PySCeS was also the first tool that implemented extensive support for structural analysis[10] of the stoichiometry matrix.

A number of reviews and commentaries have been written that discuss pySCeS:

  • Sauro and Bergmann[11] discuss a very of simulation platforms with a specific section on pySCeS.
  • Schlozel et al[12] discuss a variety of text-based languages, including pySCeS.
  • Vallabhajosyula et al[13], discuss the stoichiometry analysis algorithms used by pySCeS.

See also

  • List of systems biology modeling software

References

  1. Olivier, Brett; Rohwer, Johann; Hofmeyr, Jan-Hendrik (2005). "Modelling cellular systems with PySCeS". Bioinformatics 21 (4): 560-561. doi:10.1093/bioinformatics/bti046. 
  2. "PySCeS: The Python Simulator for Cellular Systems" (in en). https://pysces.sourceforge.net/. 
  3. Hindmarsh, A. C (1983). ODEPACK, a systematized collection of ODE solvers. Amsterdam North-Holland. p. 55-64. 
  4. Moré, J. J; Garbow, B. S; Hillstrom, K. E. (1980). User Guide for MINPACK-1. Argonne National Laboratory Report ANL-80-74, Argonne, Ill. http://cds.cern.ch/record/126569/files/CM-P00068642.pdf. 
  5. Deuflhard, P (2004). Newton Methods for Nonlinear Problems. Springer-Verlag, NY. 
  6. Hofmeyr, Jannie (2001). "Metabolic control analysis in a nutshell" (in en). Proceedings of the 2nd International Conference on Systems Biology. https://www.semanticscholar.org/paper/Metabolic-control-analysis-in-a-nutshell-Hofmeyr-Hucka/ab5bbdf2d261d098d5584fa8074d80664b578ef2. 
  7. Kerkhoven, Eduard J.; Achcar, Fiona; Alibu, Vincent P.; Burchmore, Richard J.; Gilbert, Ian H.; Trybiło, Maciej; Driessen, Nicole N.; Gilbert, David et al. (5 December 2013). "Handling Uncertainty in Dynamic Models: The Pentose Phosphate Pathway in Trypanosoma brucei". PLoS Computational Biology 9 (12): e1003371. doi:10.1371/journal.pcbi.1003371. 
  8. Rheinboldt, Werner C.; Burkardt, John V. (June 1983). "A locally parameterized continuation process". ACM Transactions on Mathematical Software 9 (2): 215–235. doi:10.1145/357456.357460. 
  9. Pillay, Ché S.; Eagling, Beatrice D.; Driscoll, Scott R.E.; Rohwer, Johann M. (July 2016). "Quantitative measures for redox signaling". Free Radical Biology and Medicine 96: 290–303. doi:10.1016/j.freeradbiomed.2016.04.199. 
  10. Reder, Christine (November 1988). "Metabolic control theory: A structural approach". Journal of Theoretical Biology 135 (2): 175–201. doi:10.1016/S0022-5193(88)80073-0. 
  11. Sauro, Herbert M.; Bergmann, Frank T. (2010). "Software Tools for Systems Biology". Systems Biomedicine: 289–314. doi:10.1016/B978-0-12-372550-9.00012-2. 
  12. Schölzel, Christopher; Blesius, Valeria; Ernst, Gernot; Dominik, Andreas (December 2021). "Characteristics of mathematical modeling languages that facilitate model reuse in systems biology: a software engineering perspective". npj Systems Biology and Applications 7 (1): 27. doi:10.1038/s41540-021-00182-w. 
  13. Vallabhajosyula, R. R.; Chickarmane, V.; Sauro, H. M. (1 February 2006). "Conservation analysis of large biochemical networks". Bioinformatics 22 (3): 346–353. doi:10.1093/bioinformatics/bti800. 

External links

Category:Systems biology Category:Ordinary differential equations Category:Software using the BSD license