Department of Chemical Engineering and Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 25606, USA.
Biotechnol Prog. 2012 Jan-Feb;28(1):33-44. doi: 10.1002/btpr.704. Epub 2011 Sep 26.
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.
细胞内的信息流是由一系列蛋白质-蛋白质相互作用控制的，可以将其描述为一个反应网络。生化反应网络的数学模型可以通过重复应用特定的规则来构建，这些规则定义了反应物如何相互作用以及反应中形成了哪些新的物种。为了帮助理解潜在的生物化学，时间尺度分析是一种用于缩小反应网络规模的方法。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一个简单的受体-配体结合模型和一个基于规则的幼稚 CD4+T 细胞中白细胞介素 12（IL-12）信号转导的模型。IL-12 信号通路包括多个蛋白质-蛋白质相互作用，这些相互作用共同传递信息；然而，基于现有数据，还没有证明足以捕捉观察到的动力学的机制细节水平是合理的。分析正确地预测了与 Janus Kinase 2 和 Tyrosine Kinase 2 与其相应受体结合相关的反应处于准平衡状态。相比之下，与配体结合和受体周转率相关的反应调节细胞对 IL-12 的反应。使用经验贝叶斯方法估计了时间尺度的不确定性。这种方法补充了现有的基于排名和通量的方法，可以用于研究复杂的反应网络。最终，基于规则的模型的时间尺度分析是一种计算工具，可以用于揭示调节信号转导动力学的生化步骤。
