Department of Computer Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America.
PLoS One. 2009 Dec 1;4(12):e8040. doi: 10.1371/journal.pone.0008040.
A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.
系统生物学的一个主要挑战是开发对特定细胞系统的功能和行为的详细动态理解，这取决于特定网络中的元素及其相互关系。计算建模在网络动力学研究和揭示潜在机制中起着不可或缺的作用。在这里，我们提出了一种系统方法，该方法将离散动态建模和实验数据相结合，以重建特定于表型的细胞信号转导网络。对肝细胞中胰岛素信号系统的动态分析提供了所提出方法的概念验证应用。我们小组最近发现双链 RNA 依赖性蛋白激酶 (PKR) 在胰岛素信号网络中起着重要作用。胰岛素信号网络的动态行为由各种反馈途径进行调整，其中许多途径有可能与 PKR 进行串扰。鉴于胰岛素信号的复杂性，通过实验测试网络中所有可能的相互作用来确定哪些途径在我们的细胞系统中起作用是效率低下的。我们的离散动态模型提供了一个计算模型框架，该框架集成了潜在的相互作用，并评估了各种相互作用对信号网络动态行为的贡献。使用模型进行的模拟针对网络在受到干扰时的响应生成了可测试的假设，然后对其进行了实验评估，以确定在我们特定的肝细胞系统中起作用的途径。建模与实验结果相结合，增强了我们对胰岛素信号动力学的理解，并有助于生成特定于上下文的信号网络。
