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