A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network.
A U-system approach for predicting metabolic behaviors and responses based on an alleged metabolic reaction network.
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DOI：
10.1186/1752-0509-8-s5-s4
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发表时间：
2014
影响因子：
--
通讯作者：
Hirai MY
中科院分区：
文献类型：
--
作者：
Sriyudthsak K;Sawada Y;Chiba Y;Yamashita Y;Kanaya S;Onouchi H;Fujiwara T;Naito S;Voit EO;Shiraishi F;Hirai MY
系统生物学的进展为全面理解生物系统提供了复杂的方法。然而，由于难以从自然受到生物波动影响的实验数据确定合适的模型参数值，计算分析受到阻碍。数据也可能受到实验不确定性的影响，有时不包含有关因技术原因无法测量的变量的所有信息。我们在这里展示了一种构建粗略模型的简化方法，该方法使我们能够用最少的输入信息建立动态模型。该方法在生化系统理论 (BST) 框架内使用纯质量作用系统和广义质量作用 (GMA) 系统的混合体，速率常数为 1，正常动力学阶数为 1，抑制和激活效应为 -0.5 和 0.5，称为 Unity (U) 系统。 U 系统模型不一定适合所有数据，但通常足以预测无法同时测量的代谢物的代谢行为，识别实验数据与假设的潜在途径结构之间的不一致，以及预测系统对基因或酶修饰的反应。 U 系统方法通过小型通用系统进行了验证，并用于模拟高等植物拟南芥的大规模代谢反应网络。通过预测模拟获得的动态行为与实际可用的代谢组时间序列数据一致，识别了实验数据集中可能的错误，并以定性方式估计了不可测量的代谢物的可能行为。该模型还可以预测由于基因改造而改变网络结构的拟南芥的代谢反应。 U 系统方法可以根据所谓的代谢反应网络的结构有效地预测代谢行为和反应。因此，它可以成为数据分析、模型诊断的有用一线工具，并有助于下一步实验的设计。
Progress in systems biology offers sophisticated approaches toward a comprehensive understanding of biological systems. Yet, computational analyses are held back due to difficulties in determining suitable model parameter values from experimental data which naturally are subject to biological fluctuations. The data may also be corrupted by experimental uncertainties and sometimes do not contain all information regarding variables that cannot be measured for technical reasons. We show here a streamlined approach for the construction of a coarse model that allows us to set up dynamic models with minimal input information. The approach uses a hybrid between a pure mass action system and a generalized mass action (GMA) system in the framework of biochemical systems theory (BST) with rate constants of 1, normal kinetic orders of 1, and -0.5 and 0.5 for inhibitory and activating effects, named Unity (U)-system. The U-system model does not necessarily fit all data well but is often sufficient for predicting metabolic behavior of metabolites which cannot be simultaneously measured, identifying inconsistencies between experimental data and the assumed underlying pathway structure, as well as predicting system responses to a modification of gene or enzyme. The U-system approach was validated with small, generic systems and implemented to model a large-scale metabolic reaction network of a higher plant, Arabidopsis. The dynamic behaviors obtained by predictive simulations agreed with actually available metabolomic time-series data, identified probable errors in the experimental datasets, and estimated probable behavior of unmeasurable metabolites in a qualitative manner. The model could also predict metabolic responses of Arabidopsis with altered network structures due to genetic modification. The U-system approach can effectively predict metabolic behaviors and responses based on structures of an alleged metabolic reaction network. Thus, it can be a useful first-line tool of data analysis, model diagnostics and aid the design of next-step experiments.