重新发现成对相互作用的力量
Rediscovering the power of pairwise interactions
摘要：
最近的两个工作流表明，成对相互作用可能足以捕获从蛋白质结构到神经元网络的生物系统的复杂性。在一种方法中，蛋白质家族中可能的氨基酸序列通过“哈密尔顿算子”的蒙特卡罗退火产生，所述哈密尔顿算子迫使氨基酸取代之间的成对相关性接近观察到的相关性。在另一种方法中，观察到的神经元对之间的相关性被用来构建一个最大熵模型的网络作为一个整体的状态。我们表明，在一定的限制，这两种方法在数学上是等价的，我们评论这个框架提出的开放问题
英文摘要：
Two recent streams of work suggest that pairwise interactions may be sufficient to capture the complexity of biological systems ranging from protein structure to networks of neurons. In one approach, possible amino acid sequences in a family of proteins are generated by Monte Carlo annealing of a "Hamiltonian" that forces pairwise correlations among amino acid substitutions to be close to the observed correlations. In the other approach, the observed correlations among pairs of neurons are used to construct a maximum entropy model for the states of the network as a whole. We show that, in certain limits, these two approaches are mathematically equivalent, and we comment on open problems suggested by this framework
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