Inferring a network from dynamical signals at its nodes.-猫眼课题宝

DOI：

10.1371/journal.pcbi.1008435

发表时间：

2020-11

期刊：

PLoS computational biology

影响因子：

4.3

通讯作者：

中科院分区：

生物学2区

文献类型：

作者：

Weistuch C;Agozzino L;Mujica-Parodi LR;Dill KA

来源链接：

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728228/

我们给出了一个近似的解决方案的困难的逆问题推断的未知网络的拓扑结构，从给定的时间相关的信号在节点。例如，我们测量大脑中单个神经元的信号，并推断它们是如何相互连接的。我们使用最大口径作为推理原则。使用两种不同的近似成对耦合的高维数据的组合的挑战。我们证明了两个原则：在一个非线性遗传拨动开关电路，并在一个玩具神经网络。主要的科学兴趣是网络互联网，商业供应链，社交媒体，交通，细胞内的生化反应，大脑中的神经元，以及许多其他。通常，挑战是在网络的有限数量的节点上测量一些速率，并尝试推断有关复杂网络及其在不同条件下的流模式的更多信息。在这里，我们设计了一种数学方法来推断这种网络的动态，只有有限的实验信息。最适合此目的的工具是最大口径原则，但它也要求我们应对现实世界网络的高维挑战。我们给出了两个层次的近似，减少这一简单的问题，推断每个节点的动态单独。我们表明，这些近似提供了新的见解和准确的推论，并有希望为大规模的生物物理和其他网络的推论。

We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they are inter-connected. We use Maximum Caliber as an inference principle. The combinatorial challenge of high-dimensional data is handled using two different approximations to the pairwise couplings. We show two proofs of principle: in a nonlinear genetic toggle switch circuit, and in a toy neural network. Of major scientific interest are networks—the internet, commercial supply chains, social media, traffic, biochemical reactions inside cells, the neurons in the brain, and many others. Often, the challenge is to measure a few rates at a limited number of nodes of the network, and to try to infer more information about a complex network and its flow patterns under different conditions. Here we devise a mathematical method to infer the dynamics of such networks, given only limited experimental information. The tool best suited for this purpose is the Principle of Maximum Caliber, but it also requires that we handle the challenge of the high-dimensionality of real-world nets. We give two levels of approximation that reduce this to the simpler problem of inferring the dynamics of each node individually. We show that these approximations provide novel insights and accurate inferences and are promising for drawing inferences about large-scale biophysical and other networks.