Timescale analysis of rule-based biochemical reaction networks.
Timescale analysis of rule-based biochemical reaction networks.
复制标题
基于规则的生化反应网络的时间尺度分析。
DOI：
10.1002/btpr.704
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发表时间：
2012
影响因子：
2.9
通讯作者：
Finley,StaceyD
中科院分区：
文献类型：
--
作者：
Klinke2nd,DavidJ;Finley,StaceyD
细胞内的信息流由一系列蛋白质-蛋白质相互作用控制，这些相互作用可以被描述为反应网络。生物化学反应网络的数学模型可以通过重复应用定义反应物如何相互作用以及在反应中形成什么新物种的特定规则来构建。为了帮助理解潜在的生物化学，时间尺度分析是一种用于修剪反应网络大小的方法。在这项工作中，我们将与时间尺度分析相关的方法扩展到反应规则，而不是网络中包含的物种。为了说明这种方法，我们将时间尺度分析应用于简单的受体-配体结合模型和幼稚CD 4 + T细胞中白细胞介素-12（IL-12）信号传导的基于规则的模型。IL-12信号通路包括共同传递信息的多种蛋白质-蛋白质相互作用;然而，根据现有数据，尚未证明足以捕获所观察到的动力学的机制细节水平。该分析正确预测了与Janus激酶2和酪氨酸激酶2结合其相应受体相关的反应存在于伪平衡状态。相比之下，与配体结合和受体转换相关的反应调节对IL-12的细胞应答。使用经验贝叶斯方法估计时间尺度的不确定性。这种方法补充了现有的基于秩和通量的方法，可用于查询复杂的反应网络。最终，基于规则的模型的时间尺度分析是一种计算工具，可用于揭示调节信号动力学的生化步骤。© 2011美国化学工程师学会生物技术。程序：2012
The flow of information within a cell is governed by a series of protein–protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed on reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor–ligand binding model and a rule‐based model of interleukin‐12 (IL‐12) signaling in naïve CD4+ T cells. The IL‐12 signaling pathway includes multiple protein–protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based on the available data. The analysis correctly predicted that reactions associated with Janus Kinase 2 and Tyrosine Kinase 2 binding to their corresponding receptor exist at a pseudo‐equilibrium. By contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL‐12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank‐ and flux‐based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule‐based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. © 2011 American Institute of Chemical Engineers Biotechnol. Prog., 2012