Exploring the landscape of model representations
Exploring the landscape of model representations
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DOI：
10.1073/pnas.2000098117
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发表时间：
2020-09-29
影响因子：
11.1
通讯作者：
Noid, W. G.
中科院分区：
文献类型：
--
作者：
Foley, Thomas T.;Kidder, Katherine M.;Noid, W. G.
任何物理模型的成功关键取决于对感兴趣的现象采用适当的表示法。不幸的是，要确定描述复杂现象的基本自由度或相应的适当序参数仍然是一项具有挑战性的工作。在这里，我们开发了一个统计物理框架，用于探索和定量表征表示物理系统的序参数空间。具体地说，我们检查了与基于粒子的粗粒度(CG)模型相对应的低分辨率表示空间，该模型用于蛋白质波动的简单微观模型。我们使用蒙特卡罗(MC)方法对这个空间进行采样，并确定CG表示的态密度，作为它们保存微观模型的组态信息I和大尺度涨落Q的能力的函数。这两个度量在高分辨率表示中不相关，但在较低分辨率下变得反相关。此外，我们的MC模拟表明了粗粒蛋白质的紧急长度范围，以及蛋白质表示的好和坏之间的定性区别。最后，我们将我们的工作与最近的聚类图和检测网络社区的方法联系起来。
The success of any physical model critically depends upon adopting an appropriate representation for the phenomenon of interest. Unfortunately, it remains generally challenging to identify the essential degrees of freedom or, equivalently, the proper order parameters for describing complex phenomena. Here we develop a statistical physics framework for exploring and quantitatively characterizing the space of order parameters for representing physical systems. Specifically, we examine the space of low-resolution representations that correspond to particlebased coarse-grained (CG) models for a simple microscopic model of protein fluctuations. We employ Monte Carlo (MC) methods to sample this space and determine the density of states for CG representations as a function of their ability to preserve the configurational information, I, and large-scale fluctuations, Q, of the microscopic model. These two metrics are uncorrelated in high-resolution representations but become anticorrelated at lower resolutions. Moreover, our MC simulations suggest an emergent length scale for coarse-graining proteins, as well as a qualitative distinction between good and bad representations of proteins. Finally, we relate our work to recent approaches for clustering graphs and detecting communities in networks.