satpreetsingh/sirisaac

Automated dynamical systems inference

SirIsaac

Automated dynamical systems inference.

Main goal: Given experimental dynamical systems trajectories, find a dynamical system that can predict future trajectories.

References

The theory and rationale behind the SirIsaac approach, as well as example use cases, are described in the following publications:

Daniels, B. C., & Nemenman, I. (2015). Automated adaptive inference of phenomenological dynamical models. Nature Communications, 6, 8133.
https://doi.org/10.1038/ncomms9133

Daniels, B. C., & Nemenman, I. (2015). Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression. Plos One, 10(3), e0119821.
https://doi.org/10.1371/journal.pone.0119821

Dependencies

Python 2.6 or later (not Python 3)
Scipy
Matplotlib
(One way to install the above is with Anaconda or Sage. See Installation.md.)

SloppyCell (http://sloppycell.sourceforge.net)

Optional dependencies

Pypar (for running on multiple processors)
SBML (systems biology markup language)
BioNetGen Pygraphviz (for creating network diagrams)
ipython (for reading ipython notebook file describing example usage)

Contributors

Bryan Daniels, Ilya Nemenman