Identifying dynamical systems with bifurcations from noisy partial observation
Yohei Kondo, Kunihiko Kaneko, and Shuji Ishihara
Accepted
We propose a statistical machine-learning approach to derive low-dimensional models by integrating noisy time-series data from partial observation of high-dimensional systems, aiming to utilize quantitative data on biological phenomena in the cell. In particular, the method estimates a model from data at different values of a bifurcation parameter, in order to characterize biological functions as bifurcation types that are insensitive to system details and to experimental errors. The method is tested using artificial data generated from two cell-cycle control system models that exhibit different bifurcations, and the learned systems are shown to robustly inherit the bifurcation types.