Automated dynamical systems inference.
Main goal: Given experimental dynamical systems trajectories, find a dynamical system that can predict future trajectories.
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
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)
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)
Bryan Daniels, Ilya Nemenman