Automated, predictive, and interpretable inference.
Automated, predictive, and interpretable inference of Caenorhabditis elegans escape dynamics.
Significance
The cost of an empirical bit in biophysics has fallen dramatically, and high-precision data are now abundant. However, biological systems are notoriously complex, multiscale, and inhomogeneous, so that we often lack intuition for transforming such measurements into theoretical frameworks. Modern machine learning can be used as an aid. Here we apply our Sir Isaac platform for automatic inference of a model of the escape response behavior in a roundworm directly from time series data. The automatically constructed model is more accurate than that curated manually, is biophysically interpretable, and makes nontrivial predictions about the system.
Daniels BC, Ryu WS, Nemenman I. Automated, predictive, and interpretable inference of Caenorhabditis elegans escape dynamics. Proc Natl Acad Sci U S A. 2019 Apr 9;116(15):7226-7231. doi: 10.1073/pnas.1816531116.