  The roundworm C. elegans exhibits robust escape behavior in response to
rapidly rising temperature. The behavior lasts for a few seconds, shows history
dependence, involves both sensory and motor systems, and is too complicated to
model mechanistically using currently available knowledge. Instead we model the
process phenomenologically, and we use the Sir Isaac dynamical inference
platform to infer the model in a fully automated fashion directly from
experimental data. The inferred model requires incorporation of an unobserved
dynamical variable, and is biologically interpretable. The model makes accurate
predictions about the dynamics of the worm behavior, and it can be used to
characterize the functional logic of the dynamical system underlying the escape
response. This work illustrates the power of modern artificial intelligence to
aid in discovery of accurate and interpretable models of complex natural
systems.
