Building a microscopically accurate model of a system as complex as a brain or even a single cell is hardly possible, and arguably not very useful. Can we use modern machine learning approaches to automate finding predictive phenomenological dynamical models of time series measured in experiments? I will discuss our approach to the problem, implemented as a software Package SirIsaac. I will show how the method performs in various synthetic test cases, as well as on building (and interpreting) a dynamical model of a reflexive escape from a painful stimulus by C. elegans.