PyDSTool logo    


Dynamical systems modeling, simulation and analysis environment


PyDSTool is a sophisticated & integrated simulation and analysis environment for dynamical systems models of physical systems (ODEs, DAEs, maps, and hybrid systems).

PyDSTool is platform independent, written primarily in Python with some underlying C and Fortran legacy code for fast solving. It makes extensive use of the numpy and scipy libraries. PyDSTool supports symbolic math, optimization, phase plane analysis, continuation and bifurcation analysis, data analysis, and other tools for modeling -- particularly for biological applications.

The project is fully open source with a BSD license, and welcomes contributions from the community. Please visit the support pages at to post questions and feedback.

Support for Python 3.3+ and 64 bit OS platforms in v0.90! (2015)

These documentation pages are still under development. In the meantime, please feel free to ask questions on the support pages or email the developers. A custom site search is provided below.


If you use PyDSTool in your published work, please cite it in the following way:

Clewley R, Hybrid Models and Biological Model Reduction with PyDSTool, PLoS Computational Biology 8(8): e1002628 (2012)

Clewley RH, Sherwood WE, LaMar MD, Guckenheimer JM (2007) PyDSTool, a software environment for dynamical systems modeling. URL


Screenshot (see the PyCont page for details):



Recent citations of PyDSTool (pre-2014) show its use in a wide variety of disciplines:

  • Guckenheimer, J. and M.D. Lamar, Periodic orbit continuation in multiple time scale systems, in Numerical continuation methods for dynamical systems, B. Krauskopf, H.M. Osinga, and J. Galan-Vioque, Editors. 2007, Springer Netherlands. p. 253-267.
  • Meijer, H., F. Dercole, and B. Oldeman, Numerical bifurcation analysis, in Encyclopedia of complexity and systems science. 2009, Springer Verlag. p. 6329-6352.
  • Hui, L.H. and P.Y. Fong. A numerical study of ship's rolling motion. in Proceedings of the 6th IMT-GT Conference on Mathematics, Statistics and its Applications (ICMSA2010). 2010. Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia.
  • McInnes, A. and B. Thorne. SciPySim: Towards distributed heterogeneous system simulation for the SciPy platform. in TMS-DEVS’11 Proceedings of the 2011 Symposium on Theory of Modeling & Simulation. 2011.
  • Krauskopf, B., H. Osinga, and J. Galan-Vioque, eds. Numerical continuation methods for dynamical systems: Path following and boundary value problems. 2007, Springer.
  • Liu, T. and D.A. Lauffenburger, eds. Systems biomedicine: Concepts and perspectives. 2009, Associated Press.
  • Budisic, M., Applied Koopmanism. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2012. 22(4).
  • Budisic, M. and I. Mezic, Geometry of the ergodic quotient reveals coherent structures in flows. Physica D, 2012. 241(15): p. 1255-1269.
  • De Vos, D., A. Dzhurakhalov, D. Draelants, I. Bogaerts, S. Kalve, E. Prinsen, K. Vissenberg, W. Vanroose, J. Broeckhove, and G.T. Beemster, Towards mechanistic models of plant organ growth. Journal of Experimental Botany, 2012. 63(9): p. 3325-3337.
  • Hong, T., J. Xing, L. Li, and J.J. Tyson, A mathematical model for the reciprocal differentiation of T helper 17 cells and induced regulatory T cells. PLoS Comput Biol, 2011. 7: p. e1002122.
  • Hong, T., J. Xing, L. Li, and J.J. Tyson, A simple theoretical framework for understanding heterogeneous differentiation of CD4+ T cells. BMC Systems Biology, 2012. 6(66).
  • Kidd, P. and N. Wingreen, Modeling the role of covalent enzyme modification in Escherichia coli nitrogen metabolism. Physical Biology, 2010. 7(1): p. 016006.
  • Luthi, M.P., A. Bauder, and M. Funk, Volume change reconstruction of Swiss glaciers from length change data. Journal of Geophysical Research, 2010. 115: p. F04022.
  • Marin, B., W. Barnett, A. Doloc-Mihu, R. Calabrese, and G. Cymbalyuk, High prevalence of multistability of rest states and bursting in a database of a model neuron. PLoS Comput Biol, 2013. 9(3): p. e1002930.
  • Myers, C.R., R.N. Gutenkunst, and J.P. Sethna, Python unleashed on systems biology, Computing in Science & Engineering, 2007. 9(3): p. 34-37.
  • Rothkegel, A. and K. Lehnertz, Conedy: A scientific tool to investigate complex network dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2012. 22(1): p. 013125-8.
  • Roy, D., A. Ghosh, and V.K. Jirsa, Phase description of spiking neuron networks with global electric and synaptic coupling. Physical Review E, 2011. 83: p. 051909.
  • Sherwood, W.E. and J. Guckenheimer, Dissecting the phase response of a model bursting neuron. SIAM J. Appl. Dyn. Syst., 2010. 9(3): p. 659-703.
  • Van Vaerenbergh, T., M. Fiers, P. Mechet, T. Spuesens, R. Kumar, G. Morthier, B. Schrauwen, J. Dambre, and P. Bienstman, Cascadable excitability in microrings. Optics Express, 2012. 20(18): p. 20292-20308.


Latest version: 0.90.2