Exploring the landscape of model representations

Abstract

SignificancePhysical phenomena can often be described by surprisingly few order parameters. Unfortunately, it is challenging to identify these essential degrees of freedom. Here we develop a statistical physics framework for exploring the landscape of order parameters, or coarse-grained representations, for a microscopic protein model. We employ Monte Carlo methods to statistically characterize this landscape. We define metrics assessing the intrinsic quality of each representation for preserving the configurational information and large-scale motions of the underlying microscopic model. Interestingly, these metrics are anticorrelated in low-resolution representations. Moreover, below a critical resolution, a phase transition qualitatively distinguishes superior and inferior representations. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.