Dynamic analysis of integrated signaling, metabolic, and regulatory networks.
Dynamic analysis of integrated signaling, metabolic, and regulatory networks.
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DOI：
10.1371/journal.pcbi.1000086
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发表时间：
2008-05-23
影响因子：
4.3
通讯作者：
Papin, Jason A.
中科院分区：
文献类型：
--
作者：
Lee, Jong Min;Gianchandani, Erwin P.;Eddy, James A.;Papin, Jason A.
细胞外信号影响信号传导、代谢和调节过程以引起细胞反应。尽管细胞内信号传导、代谢和调控网络是高度集成的，但以前的分析主要集中在独立的过程（例如，代谢），而不考虑它们之间存在的相互作用。然而，有证据表明，许多疾病是由多功能成分引起的，这些成分在信号传导、代谢和调节网络中发挥作用。因此，在这项研究中，我们提出了一种基于通量平衡分析（FBA）的策略，称为集成动态FBA（idFBA），动态模拟细胞表型所产生的集成网络。idFBA框架需要信号传导、代谢和调控过程的综合化学计量重建。它假设准稳态条件下的“快速”反应，并将“慢”反应的化学计量形式主义在一个时间延迟的方式。为了评估idFBA的功效，我们开发了一种原型集成系统，其包括信号传导、代谢和调节过程，具有实际系统的网络特征，并基于文献中观察到的典型时间尺度并入动力学参数。将idFBA应用于原型系统，针对不同的环境和基因调控规则对其进行评估。此外，我们以类似的方式将idFBA框架应用于单细胞真核生物酿酒酵母的代表性模块。最终，idFBA促进了细胞外信号对细胞表型的系统性影响的定量、动态分析，并在与等效动力学模型对比时产生了可比较的时程预测。由于idFBA解决了线性编程问题，并且不需要详尽的动力学参数列表，因此可以有效地将其扩展到整合的细胞内系统，所述整合的细胞内系统在基因组规模上并入信号传导、代谢和调节过程，例如S.酿酒系统介绍。细胞系统包括许多不同的组件和组件之间的相互作用，跨越信号转导，转录调控和代谢。虽然信号，代谢和调节活动往往是相互独立的研究，有越来越多的证据表明，相当大的相互作用发生在他们之间，这种相互作用的故障与疾病。集成网络的计算分析一直具有挑战性，因为涉及的时间尺度以及这种系统的绝对量级（例如，所涉及的速率常数的数目）。为此，我们开发了一种新的计算框架，称为集成动态通量平衡分析（idFBA），生成跨越信号传导，调节和代谢过程的物种浓度的定量，动态预测。idFBA扩展了称为通量平衡分析（FBA）的现有方法，因为它将“快”和“慢”反应结合起来，从而促进了全细胞表型的研究，而不仅仅是亚细胞网络特性。我们将这个框架应用于来自文献的原型集成系统以及代表性的集成酵母模块（高渗透压甘油[HOG]途径），并生成与可用实验数据相匹配的时间过程预测。通过将该框架扩展到更大规模的系统，可以迅速获得全细胞系统的表型特征。
Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)–based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for “fast” reactions and incorporates “slow” reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporating kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and regulatory processes at the genome scale, such as the S. cerevisiae system presented here. Cellular systems comprise many diverse components and component interactions spanning signal transduction, transcriptional regulation, and metabolism. Although signaling, metabolic, and regulatory activities are often investigated independently of one another, there is growing evidence that considerable interplay occurs among them, and that the malfunctioning of this interplay is associated with disease. The computational analysis of integrated networks has been challenging because of the varying time scales involved as well as the sheer magnitude of such systems (e.g., the numbers of rate constants involved). To this end, we developed a novel computational framework called integrated dynamic flux balance analysis (idFBA) that generates quantitative, dynamic predictions of species concentrations spanning signaling, regulatory, and metabolic processes. idFBA extends an existing approach called flux balance analysis (FBA) in that it couples “fast” and “slow” reactions, thereby facilitating the study of whole-cell phenotypes and not just sub-cellular network properties. We applied this framework to a prototypic integrated system derived from literature as well as a representative integrated yeast module (the high-osmolarity glycerol [HOG] pathway) and generated time-course predictions that matched with available experimental data. By extending this framework to larger-scale systems, phenotypic profiles of whole-cell systems could be attained expeditiously.
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影响因子：
3.4
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