Many molecular process in biology involve huge ranges of length and time scales that require coarse-grained rather than detailed atomic-resolution computer simulations.  Yet, there remain many fundamental questions regarding how such simplified models should be developed.  Are there theoretically intuitive and numerically robust strategies for turning small-scale all-atom simulations into coarse models suitable for large-scale modeling? How can we identify what atomic details are unnecessary and can be discarded?  Are there systematic ways to detect emergent physics?  Here we discuss a fundamentally new approach to this problem.  We propose that a natural way of viewing the coarse-graining problem is in terms of information theory.  A quantity called the relative entropy measures the information lost upon coarse graining and hence the (inverse) fitness of a particular coarse-grained model.  Minimization of the relative entropy thus provides a sort-of universal variational principle for coarse-graining, and a way to �automatically� discover and generate coarse models of many systems.  We show that this new approach enables us to develop very simple but surprisingly accurate models of water, hydrophobic interactions, self-assembling peptides, and proteins that enable new physical insights as well as simulations of large-scale interactions.  We discuss both theoretical and numerical aspects of this approach, in particular highlighting a new coarse-graining algorithm that efficiently optimizes coarse-grained models with even thousands of free parameters.

Information-based approach to coarse-grained models in molecular biophysics

244th National Meeting (2012)

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