Gfeller David, de Lachapelle David Morton, De Los Rios Paolo, Caldarelli Guido, Rao Francesco
Laboratoire de Biophysique Statistique, SB/ITP, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne, Switzerland.
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Aug;76(2 Pt 2):026113. doi: 10.1103/PhysRevE.76.026113. Epub 2007 Aug 27.
The configuration space network (CSN) of a dynamical system is an effective approach to represent the ensemble of configurations sampled during a simulation and their dynamic connectivity. To elucidate the connection between the CSN topology and the underlying free-energy landscape governing the system dynamics and thermodynamics, an analytical solution is provided to explain the heavy tail of the degree distribution, neighbor connectivity, and clustering coefficient. This derivation allows us to understand the universal CSN topology observed in systems ranging from a simple quadratic well to the native state of the beta3s peptide and a two-dimensional lattice heteropolymer. Moreover, CSNs are shown to fall in the general class of complex networks described by the fitness model.
动态系统的构型空间网络(CSN)是一种有效的方法,用于表示模拟过程中采样的构型集合及其动态连通性。为了阐明CSN拓扑结构与支配系统动力学和热力学的潜在自由能景观之间的联系,提供了一种解析解来解释度分布、邻域连通性和聚类系数的重尾现象。这一推导使我们能够理解在从简单二次阱到β3s肽的天然状态以及二维晶格杂聚物等系统中观察到的通用CSN拓扑结构。此外,CSN被证明属于由适应度模型描述的复杂网络的一般类别。