The Tellurium Simulator
Funded by NIGMS: R01-GM123032 and previously from R01-GM081070
A Python Environment for Reproducible Dynamical Modeling of Biological Networks
Tellurium is a Python package that knits together a variety of important packages for carrying out simulation studies in systems biology and other disciplines.
Tellurium provides an interface to the powerful high-performance libroadrunner simulation engine.
Tellurium allows you to build your models using an easy-to-use human-readable version of SBML called Antimony (see also https://github.com/sys-bio/antimony). Antimony Tutorial
Tellurium supports all the major standards such as SBML, SED-ML, and COMBINE archives.
Tellurium can be used via GUI front-ends such as Spyder, PyCharm, or Jupyter Notebooks (including CoLab) with support for advanced productivity and interactive editing features
Installation is via standard pip installation. We also provide a one-click installer for Windows uses which provides a complete environment for systems biology modeling
Tellurium runs on Windows, Mac OS and Linux.
Want to know more? Visit the documentation site at:
Documentation
You can join discussions at:
https://groups.google.com/g/tellurium-discuss
or you can submit questions and issues to
https://github.com/sys-bio/tellurium/issues
Tellurium relies on open-source contributions from many people. See the acknowledgment page for details.
New contributions are always welcome, see the GitHub page.
The Tellurium, Antimony, and libRoadRunner project were funded by NIH/NIGMS (GM081070). Previous funding has come from DARPA, DOE, and the NSF.
Tellurium is licensed under the Apache 2.0 license. Constituent packages have various licenses.
Recent Publications:
Acknowledgements
This work is funded by the generous support of the NIH/NIGMS grant: GM123032, and GM081070
Example Python script, modeling a simple three-step pathway:
import tellurium as te
r = te.loada ('''
Xo -> S1; k1*Xo
S1 -> S2; k2*S1
S2 ->; k3*S2
k1 = 0.1; k2 = 0.05; k3 = 0.1
Xo = 10; S2 = 0
''')
m = r.simulate (0, 80, 100)
r.plot()