CRCT, Université de Toulouse, Inserm, CNRS, Université Toulouse III-Paul Sabatier, Centre de Recherches en Cancérologie de Toulouse, Toulouse, France.
Barcelona Supercomputing Center, Barcelona, Spain.
PLoS Comput Biol. 2023 Aug 10;19(8):e1011254. doi: 10.1371/journal.pcbi.1011254. eCollection 2023 Aug.
Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data.
基因调控网络的推断已经成为研究的一个活跃领域，大约 20 年来，已经开发出了基于各种假设和方法的复杂推断算法。随着对更准确、更强大模型的需求不断增加，推断问题仍然是广泛的科学兴趣所在。通过基因调控网络对生物系统进行抽象表示是研究这些系统的一种强大方法，它可以编码不同数量和类型的信息。在这篇综述中，我们总结了基于时间序列转录组学的不同类型的推断算法，概述了基因调控网络在计算生物学中的主要应用。这篇综述旨在为生物学家和刚接触该主题的研究人员提供一个更新的调控网络推断工具参考，并指导他们选择最合适的推断方法，以满足他们的问题、目标和实验数据。
