Daniels and Nemenman, 2015a

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B Daniels and I Nemenman. Automated adaptive inference of phenomenological dynamical models. Nature Communications 6, 8133, 2015. PDF, arXiv.

Abstract

Dynamics of complex natural and artificial systems is often driven by large and intricate net- works of microscopic interactions, whose sheer size obfuscates understanding. In light of limited experimental data, many parameters of such dynamics are unknown, and thus models built on the detailed, mechanistic viewpoint risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Targeting modern biophysics applications, here we develop an approach that instead constructs phenomeno- logical, coarse-grained models of network dynamics that automatically adapt their complexity to the amount of available data. Such adaptive models lead to accurate predictions even when mi- croscopic details of the studied systems are unknown due to insufficient data. The approach is computationally tractable, even for a relatively large number of dynamical variables. For example, it correctly infers the phase space structure for simulated planetary motion data, avoids overfitting in a complex biological signaling system, and produces accurate predictions for a yeast glycolysis model with only tens of data points and over half of the interacting species unobserved.