École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, 44000, France.
BMC Bioinformatics. 2023 Aug 25;24(Suppl 1):321. doi: 10.1186/s12859-023-05429-3.
The impact of a perturbation, over-expression, or repression of a key node on an organism, can be modelled based on a regulatory and/or metabolic network. Integration of these two networks could improve our global understanding of biological mechanisms triggered by a perturbation. This study focuses on improving the modelling of the regulatory network to facilitate a possible integration with the metabolic network. Previously proposed methods that study this problem fail to deal with a real-size regulatory network, computing predictions sensitive to perturbation and quantifying the predicted species behaviour more finely.
To address previously mentioned limitations, we develop a new method based on Answer Set Programming, MajS. It takes a regulatory network and a discrete partial set of observations as input. MajS tests the consistency between the input data, proposes minimal repairs on the network to establish consistency, and finally computes weighted and signed predictions over the network species. We tested MajS by comparing the HIF-1 signalling pathway with two gene-expression datasets. Our results show that MajS can predict 100% of unobserved species. When comparing MajS with two similar (discrete and quantitative) tools, we observed that compared with the discrete tool, MajS proposes a better coverage of the unobserved species, is more sensitive to system perturbations, and proposes predictions closer to real data. Compared to the quantitative tool, MajS provides more refined discrete predictions that agree with the dynamic proposed by the quantitative tool.
MajS is a new method to test the consistency between a regulatory network and a dataset that provides computational predictions on unobserved network species. It provides fine-grained discrete predictions by outputting the weight of the predicted sign as a piece of additional information. MajS' output, thanks to its weight, could easily be integrated with metabolic network modelling.
通过对关键节点的干扰、过表达或抑制作用，可以基于调控和/或代谢网络对生物体的影响进行建模。整合这两个网络可以提高我们对生物体受到干扰时触发的生物学机制的整体理解。本研究重点是改进调控网络的建模，以促进与代谢网络的可能整合。以前提出的研究这个问题的方法无法处理真实大小的调控网络，无法计算对干扰敏感的预测，也无法更精细地量化预测物种的行为。
为了解决前面提到的限制，我们开发了一种基于 Answer Set Programming 的新方法 MajS。它以调控网络和离散的部分观测数据集作为输入。MajS 测试输入数据之间的一致性，提出网络的最小修复以建立一致性，最后计算网络物种的加权和有符号预测。我们通过将 HIF-1 信号通路与两个基因表达数据集进行比较来测试 MajS。我们的结果表明，MajS 可以预测 100%的未观察到的物种。当将 MajS 与两个类似的（离散和定量）工具进行比较时，我们观察到与离散工具相比，MajS 对未观察到的物种提出了更好的覆盖，对系统干扰更敏感，并且提出的预测更接近真实数据。与定量工具相比，MajS 提供了更精细的离散预测，这些预测与定量工具提出的动态一致。
MajS 是一种测试调控网络和数据集之间一致性的新方法，它为未观察到的网络物种提供计算预测。它通过输出预测符号的权重作为附加信息来提供细粒度的离散预测。由于其权重，MajS 的输出可以很容易地与代谢网络建模集成。
