Wang Yu-Chao, Chen Bor-Sen
Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan.
BMC Syst Biol. 2010 Mar 8;4:20. doi: 10.1186/1752-0509-4-20.
With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway.
In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated.
We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.
随着组学数据的不断积累，系统生物学的一个关键目标是构建不同细胞水平的网络，以研究细胞的细胞机制。然而，目前尚无令人满意的方法来构建整合基因调控网络和信号调控途径的整合细胞网络。
在本研究中，我们整合了不同类型的组学数据，并开发了一种基于耦合动态模型和统计评估构建整合细胞网络的系统方法。所提出的方法应用于酿酒酵母应激反应，阐明了酵母的应激反应机制。从高渗应激下得到的整合细胞网络中，识别出了与应激反应功能相关的高度连接的枢纽。除了高渗应激，还构建了热休克和氧化应激下的整合网络，并分析了这些网络之间的相互作用，明确了一些转录因子作为蝴蝶结结构中心决策装置的重要性以及对快速适应应激方案的关键作用。此外，还证明了所提出方法的预测能力。
我们成功构建了经文献证据验证的整合细胞网络。转录调控和蛋白质-蛋白质相互作用的整合为实际生物网络提供了更多见解，并且比未整合的网络更具预测性。该方法被证明是强大且灵活的，可用于不同条件和不同物种。整个整合细胞网络的耦合动态模型对于网络生物学和合成生物学领域的理论分析和进一步实验非常有用。
