Inferring Gene Regulatory Networks from Multiple Datasets.-猫眼课题宝

DOI：

10.1007/978-1-4939-8882-2_11

发表时间：

2018-12

期刊：

Methods in molecular biology

影响因子：

通讯作者：

中科院分区：

文献类型：

作者：

Christopher A. Penfold;Iulia Gherman;Anastasiya Sybirna;David L. Wild

高斯过程动力系统 (GPDS) 代表了推理非线性动力系统的贝叶斯非参数方法，并为从基因或蛋白质表达的多个扰动时间序列测量中学习生物网络提供了原则框架。此类方法能够捕获复杂 ODE 模型的全部丰富性，并且可以扩展以在包含数百个基因的中等大型系统中进行推理。相关的分层方法允许从多个数据集中进行推断，其中假设底层生成网络已通过网络结构、进化过程或合成操作中上下文相关的变化进行了重新连接。这些方法还可用于将实验确定的网络结构从一个物种转移到网络结构未知的另一种物种中。总的来说，这些方法提供了一个全面而灵活的平台，用于从各种数据中进行推理，并应用于系统和合成生物学以及胚胎发育的时空建模。在本章中，我们概述了 GPDS 方法，并重点介绍了它们在生物科学中的应用，并提供了来自 https://github.com/cap76/GPDS 的 Jupyter 笔记本的随附教程。

Gaussian process dynamical systems (GPDS) represent Bayesian nonparametric approaches to inference of nonlinear dynamical systems, and provide a principled framework for the learning of biological networks from multiple perturbed time series measurements of gene or protein expression. Such approaches are able to capture the full richness of complex ODE models, and can be scaled for inference in moderately large systems containing hundreds of genes. Related hierarchical approaches allow for inference from multiple datasets in which the underlying generative networks are assumed to have been rewired, either by context-dependent changes in network structure, evolutionary processes, or synthetic manipulation. These approaches can also be used to leverage experimentally determined network structures from one species into another where the network structure is unknown. Collectively, these methods provide a comprehensive and flexible platform for inference from a diverse range of data, with applications in systems and synthetic biology, as well as spatiotemporal modelling of embryo development. In this chapter we provide an overview of GPDS approaches and highlight their applications in the biological sciences, with accompanying tutorials available as a Jupyter notebook from https://github.com/cap76/GPDS .

Inferring Gene Regulatory Networks from Multiple Datasets.

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