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理解全开放反应网络的多稳态性

Understanding Multistationarity of Fully Open Reaction Networks

Research Centre for Computational Sciences and Mathematical Modelling, Coventry University(考文垂大学计算科学与数学建模研究中心)

Shenghao Yao, AmirHosein Sadeghimanesh, Matthew England

AI总结:

本文研究全开放反应网络的多稳态性,提出首个生成非多稳态网络的确定性操作,并首次利用图注意力神经网络结合新图表示预测反应网络多稳态性。

摘要:

本文研究配备质量作用动力学的全开放反应网络的多稳态性。我们改进了关于反应网络中正反馈回路存在性与多稳态性之间关系的已有结果;并提出了一种新的确定性操作,用于生成新的非多稳态网络。这一点很有意义,因为尽管已有许多操作可以从一个多稳态实例出发生成无限多个新的多稳态网络,但这是针对非多稳态对应情形的第一个此类操作。此类用于生成示例网络的工具在将数据科学应用于反应网络理论方面具有应用价值。我们通过利用这些新数据,以及一种新的反应网络图表示(该表示在物种名称置换意义下唯一),训练了一个图注意力神经网络模型来预测反应网络的多稳态性,从而证明了这一点。这是机器学习工具首次被用于研究反应网络的分类问题。

英文摘要:

This work addresses multistationarity of fully open reaction networks equipped with mass action kinetics. We improve upon the existing results relating existence of positive feedback loops in a reaction network and multistationarity; and we provide a novel deterministic operation to generate new non-multistationary networks. This is interesting because while there were many operations to create infinitely many new multistationary networks from a multistationary example, this is the first such operation for the non-multistationary counterpart. Such tools for the generation of example networks have a use-case in the application of data science to reaction network theory. We demonstrate this by using the new data, along with a novel graph representation of reaction networks that is unique up to a permutation on the name of species of the network, to train a graph attention neural network model to predict multistationarity of reaction networks. This is the first time machine learning tools are used for studying classification problems of reaction networks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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