Investigations on the integrated behavior of molecular r

分子r整合行为的研究

• 批准号: 7048175
• 负责人: KURT KOHN
• 金额: --
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7048175
• 关键词: Internet; bioinformatics; biological signal transduction; cell growth regulation; cell line; cell proliferation; computer human interaction; computer program /software; computer simulation; gene induction /repression; genetic promoter element; genetic regulatory element; hypoxia; intermolecular interaction; mathematical model; model design /development; oxygen consumption; p53 gene /protein; phosphorylation; protein protein interaction; protein structure function; protooncogene

A. Understanding the behavior of bioregulatory networks is critical for future progress in biology and therapeutics. Because of the complexity and high interconnectivity of these networks, reliable understanding can be achieved only with the aid of computer simulation studies. It is questionable however whether the present state of knowledge is sufficient for computational simulations of large systems to yield useful results. We took an alternative approach, based on the notion that essential behavior is already encoded in simple subsystems, and that further complexity serves to modulate this behavior. This possibility is attractive from considerations of evolution and system robustness. A second premise for the current investigation was that the response characteristics of the fundamental subsystems are often switch-like. We showed how this approach can facilitate linkage between theory and experiment. The subject of our investigation was the network that controls the induction of a set of genes in response to hypoxia. This was an attractive subject for theoretical study, because extensive (albeit yet incomplete) information has accumulated about relevant molecular and biological behavior, and because there is a clearly defined dependence of an output on an input. The input is the concentration of molecular oxygen, and the output is the activation of promoters that are under the control of hypoxia-regulated elements (HRE). We showed how a model ("core") subsystem can be selected from a molecular interaction map, how it can be encoded for computer survey of parameter space, how switch-like behavior can be found, and how the results may predict or guide experiments. This work was recently published (Kohn et al. 2004 Molec. Biol. Cell 15: 3042-52). We are collaborating with the Laboratory of Biosystems, CCR, NCI, which has been studying the hypoxia control system, we plan to test our theoretical predictions and to develop the theoretical model further guided by the results of experiments.B. In order to utilize most effectively the vast new information about the molecular interactions that make up the cell's regulatory networks, we urgently need a graphical method that will have the same utility that circuit diagrams serve for electronics. Complexity does not by itself make it hard to prepare diagrams. Metabolic pathway diagrams, for example, are commonly displayed on large wall charts. Bioregulatory networks, on the other hand, present special difficulties that are rarely encountered in the classical metabolic pathways: (1) function often depends on multimolecular complexes; (2) function is often modulated by multiple covalent modifications, such as phosphorylations of proteins; (3) bioregulatory proteins commonly consist of multiple domains with different functions, which may interact within the same molecule. We have developed a notation specifically suited for bioregulatory networks, and refer to the resulting diagrams as "molecular interaction maps" (because they can in fact be used like road maps to discern paths and connections) (Kohn 1999 Molec. Biol. Cell; Kohn 2001 Chaos; Aladjem et al. 2004 Science'). We have used this notation to generate maps of several key networks that function in cell proliferation control and have deposited some of them on John N. Weinstein's internet site: http://discover.nci.nih.gov. In collaboration with our LMP colleagues Mirit Aladjem and the Weinstein group, we have linked the maps with other bioinformatics databases. In addition, we are providing user-friendly features for navigation through the networks. Each molecular interaction is linked to an annotation that provides salient information and reference citations, so that the evidence bearing on each interaction can be evaluated.In addition to encyclopedic molecular interaction maps, which are analogous to road maps, we are preparing review articles that are analogous to guide-books. In these articles, we build a molecular interaction maps segmentally in a manner to illuminate functional organization, as well as provide comprehensive coverage of the known interactions. Currently in preparation are map-based reviews of the p53-Mdm2 system, the hypoxia control network, and the network centered on c-Myc.

a.了解生物调控网络的行为对于生物学和治疗学的未来进展至关重要。由于这些网络的复杂性和高度互联性，只有借助计算机模拟研究才能实现可靠的理解。然而，目前的知识水平是否足以使大型系统的计算模拟产生有用的结果是值得怀疑的。我们采取了另一种方法，其基础是基本行为已经编码在简单的子系统中，并且进一步的复杂性有助于调节这种行为。从进化和系统鲁棒性的考虑来看，这种可能性是有吸引力的。当前研究的第二个前提是，基本子系统的响应特性通常是类似开关的。我们展示了这种方法如何促进理论和实验之间的联系。我们研究的主题是控制一组基因响应缺氧诱导的网络。这是一个有吸引力的理论研究课题，因为大量的（尽管还不完整）信息积累了相关的分子和生物学行为，因为有一个明确定义的依赖于输入的输出。输入是分子氧的浓度，输出是在低氧调节元件（HRE）控制下的启动子的激活。我们展示了如何从分子相互作用图中选择模型（"核心"）子系统，如何对它进行编码以用于参数空间的计算机调查，如何发现开关样行为，以及结果如何预测或指导实验。这项工作最近发表（Kohn等人，2004年，Molec. Cell 15：3042 - 52）。我们正在与NCI生物系统实验室（CCR）合作，该实验室一直在研究缺氧控制系统，我们计划测试我们的理论预测，并在实验结果的指导下进一步开发理论模型。B.为了最有效地利用构成细胞调控网络的分子相互作用的大量新信息，我们迫切需要一种图形方法，它将具有与电路图相同的效用。复杂性本身并不会使绘制图表变得困难。例如，代谢途径图通常显示在大型挂图上。另一方面，生物调节网络提出了在经典代谢途径中很少遇到的特殊困难：（1）功能通常依赖于多分子复合物;（2）功能通常通过多个共价修饰来调节，例如蛋白质的磷酸化;（3）生物调节蛋白通常由具有不同功能的多个结构域组成，这些结构域可能在同一分子内相互作用。我们已经开发了一种特别适用于生物调控网络的符号，并将所得图称为"分子相互作用图"（因为它们实际上可以像路线图一样用于辨别路径和连接）（Kohn 1999 Molec. Cell; Kohn 2001 Chaos; Aladjem等人2004 Science）。我们已经使用这种标记法生成了几个在细胞增殖控制中起作用的关键网络的地图，并将其中一些存放在John N.在与我们的LMP同事Mirit Aladjem和Weinstein小组的合作中，我们将这些地图与其他生物信息学数据库链接起来。此外，我们还提供方便用户的网络导航功能。每个分子相互作用都与一个注释相关联，该注释提供了重要的信息和参考文献，因此可以评估与每个相互作用有关的证据。除了类似于路线图的分子相互作用图谱外，我们还准备了类似于指南的评论文章。在这些文章中，我们建立了一个分子相互作用地图分段的方式来阐明功能组织，以及提供全面覆盖的已知的相互作用。目前正在准备p53-Mdm 2系统，缺氧控制网络和以c-Myc为中心的网络的地图为基础的评论。