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\subsection{Hidden Markov models for phase inference}  In this work we developed a novel method (TODO:true?) of inferring phases from data using hidden Markov models. We found that this method is adequate to decode real-world data, as it allows to specify a noise model as well as the underlying stochastic dynamics, dynamic of the hidden states,  and to explicitly take into account features of the data like amplitude, making the hypothesis underlying the analysis more manifest. A consequence of this is that several parameters have to be calibrated. Note however that because we learn the waveform, linking the phase to the data, from a large collection of time traces, our inferred phase is robust to transformations of the data, unlike methods based on the Hilbert transform \cite{Kralemann_2008}. \subsection{Phase dynamics reconstruction as a general tool for system biology}  ?