论文标题
从非平衡稳态的扰动中推断出随机调节网络
Inferring stochastic regulatory networks from perturbations of the non-equilibrium steady state
论文作者
论文摘要
调节网络描述了分子或细胞调节剂之间的相互作用，例如转录因子和基因调节网络中的基因，激酶及其受体在信号网络中或神经网络中的神经元中的受体。定量生物学的长期目的是根据大规模数据重建此类网络。我们的目的是利用非平衡稳态围绕网络推断的波动。为此，我们使用基因调节或神经动力学的随机模型，并在高斯平均场理论中大约解决。我们基于这种随机理论开发了一种似然估计，以从网络节点的扰动数据中推断出调节性相互作用。我们将这种方法应用于人工扰动数据以及来自细胞线实验的磷酸蛋白质数据数据，并将我们的结果与限制在稳态处于平均活动的推理方案进行比较。
Regulatory networks describe the interactions between molecular or cellular regulators, like transcription factors and genes in gene regulatory networks, kinases and their receptors in signalling networks, or neurons in neural networks. A long-standing aim of quantitative biology is to reconstruct such networks on the basis of large-scale data. Our aim is to leverage fluctuations around the non-equilibrium steady state for network inference. To this end, we use a stochastic model of gene regulation or neural dynamics and solve it approximately within a Gaussian mean-field theory. We develop a likelihood estimate based on this stochastic theory to infer regulatory interactions from perturbation data on the network nodes. We apply this approach to artificial perturbation data as well as to phospho-proteomic data from cell-line experiments and compare our results to inference schemes restricted to mean activities in the steady state.
