梅林：从时间序列数据中推断代谢调节规则。

MERRIN: MEtabolic regulation rule INference from time series data.

机构信息

INRIA, CNRS, IRISA, University of Rennes, Rennes F-35000, France.

LIPME, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan F-31326, France.

出版信息

Bioinformatics. 2022 Sep 16;38(Suppl_2):ii127-ii133. doi: 10.1093/bioinformatics/btac479.

DOI:10.1093/bioinformatics/btac479

PMID:36124795

Abstract

MOTIVATION

Many techniques have been developed to infer Boolean regulations from a prior knowledge network (PKN) and experimental data. Existing methods are able to reverse-engineer Boolean regulations for transcriptional and signaling networks, but they fail to infer regulations that control metabolic networks.

RESULTS

We present a novel approach to infer Boolean rules for metabolic regulation from time-series data and a PKN. Our method is based on a combination of answer set programming and linear programming. By solving both combinatorial and linear arithmetic constraints, we generate candidate Boolean regulations that can reproduce the given data when coupled to the metabolic network. We evaluate our approach on a core regulated metabolic network and show how the quality of the predictions depends on the available kinetic, fluxomics or transcriptomics time-series data.

AVAILABILITY AND IMPLEMENTATION

Software available at https://github.com/bioasp/merrin.

SUPPLEMENTARY INFORMATION

Supplementary data are available at https://doi.org/10.5281/zenodo.6670164.

摘要

动机

已经开发了许多技术来从先验知识网络 (PKN) 和实验数据中推断布尔规则。现有的方法能够为转录和信号网络反向工程布尔规则，但它们无法推断控制代谢网络的规则。

结果

我们提出了一种从时间序列数据和 PKN 推断代谢调控布尔规则的新方法。我们的方法基于答案集编程和线性规划的组合。通过求解组合和线性算术约束，我们生成了候选布尔规则，当与代谢网络耦合时，这些规则可以再现给定数据。我们在核心调控代谢网络上评估了我们的方法，并展示了预测的质量如何取决于可用的动力学、通量组学或转录组学时间序列数据。