SirIsaac

Automated dynamical systems inference.

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

References

An example of the SirIsaac algorithm applied to experimental data appears in the following publication:

• Daniels, B. C., Ryu, W. S., & Nemenman, I. (2019). Automated, predictive, and interpretable inference of Caenorhabditis elegans escape dynamics. Proc. Natl. Acad. Sci. USA.
  https://doi.org/10.1073/pnas.1816531116

Details of the theory and rationale behind the SirIsaac approach are described here:

• 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 (https://github.com/GutenkunstLab/SloppyCell)

Optional dependencies

mpi4py (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