Investigating regulatory networks that control cell proliferation and apoptosis

研究控制细胞增殖和凋亡的调控网络

• 批准号: 7732912
• 负责人: KURT KOHN
• 金额: $ 51.26万
• 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/7732912
• 关键词: Address; Adopted; Affect; Antineoplastic Agents; Apoptosis; Attention; Behavior; Binding; Bioinformatics; Cell Cycle; Cell Death; Cell Proliferation; Cell division; Cells; Cellular biology; Chromatin; Classification; Collaborations; Communities; Complex; Computer Simulation; Computer software; Computers; Cultured Cells; DNA; DNA Damage; DNA replication fork; Databases; Defect; Development; Discipline; Electronics; Epidermal Growth Factor; Epigenetic Process; Event; Gene Mutation; Gene Proteins; Human; Individual; Internet; Journals; Kinetics; Laboratories; Learning; Link; Literature; Logic; Malignant Neoplasms; Manuals; Maps; Mediating; Metabolic; Methods; Modeling; Modification; Molecular; Mutate; Nucleosomes; Online Systems; Patients; Personal Satisfaction; Phosphorylation; Preparation; Procedures; Production; Protein p53; Publications; Publishing; Purpose; Rate; Reaction; Regulation; Relative (related person); Role; Role playing therapy; Signal Transduction; Simulate; Structure; Surveys; System; Systems Biology; TP53 gene; Testing; Thinking; Time; Update; Ursidae Family; base; cancer therapy; cell behavior; combinatorial; design; heuristics; information display; insight; interest; models and simulation; network models; novel therapeutics; programs; research study; response; simulation; stem; tool; transcription factor; tumor

An enormous amount of information has accumulated about the molecular components that govern the cell division cycle and apoptosis, information that is crucial to the development of new anti-cancer therapies. However, the relevant molecular interaction networks are so complicated that it is often difficult to understand how they function. Comprehensive network diagrams, using a standardized notation, therefore are essential in the same way that circuit diagrams are essential in electronics. Diagramming bioregulatory networks however presents a major challenge, because of the large role played by multimolecular complexes and modification contingencies . Several years ago, we devised a notation for molecular interaction maps (MIM) that many in the field now consider to be the best way to represent the networks in detail. We have used MIMs in many publications and examples have been prominently displayed in journal issues (such as being featured on the cover). To generate a MIM, one has to adhere to formal MIM notation rules (described in Kohn et al Mol. Biol. Cell 17:1-13, 2006); this requirement imposes a useful discipline of logic that enforces clear understanding of the available information about a network's structure. The MIM notation has important advantages over other proposed notations and has the unique ability to represent combinatorial complexity in molecular interaction networks (Kohn et al Mol. Systems Biol. doi:10.1038/msb4100088, 2006). In order to make MIMs conveniently accessible, we post them on the internet with links to annotations, references, and to other databases (http://discover.nci.nih.gov/mim/). During the past year, we showed for the first time how MIMs can represent network events at the DNA and chromatin levels. We prepared a MIM describing the network involved in nucleosome disassembly in front of a DNA replication fork, assembly behind the replication fork, and the copying of epigenetic information onto the replicated chromatin. We used this MIM in a comprehensive review of the molecular interactions taking place during these events, thereby showing the advantages of systematizing information in this way (Kohn et al Mol. Biol. Cell 19:1-7, 2008). We have also shown how the MIM notation can depict the events at stalled replication forks (in preparation). In the latter context, we have devised a hierarchical procedure for preparation of orderly MIMs that overcomes the haphazard manner in which network interactions are often portrayed. The p53 tumor suppressor is a key component of the DNA damage response that arrests the progress cells through the cell cycle or causes cells to undergo apoptosis. These responses are governed in large part by the activity of p53 as a transcription factor, which is controlled mainly through interactions with Mdm2 and MdmX. Although the action of Mdm2 on the control of p53 is well understood, the role of Mdmx on this control network is not. We therefore assembled an updated MIM of the interactions that comprise the p53 control network. From that MIM, we extracted for computer simulation an explicit model network that we thought could be the core of the control system involving both Mdm2 and MdmX. In simulations of the model, we surveyed the DNA damage response of p53 activity as a functions of the kinetic parameters controlling Mdm2 and MdmX. We simulated DNA damage as the rate of phosphorylation of p53, Mdm2 and MdmX. The model expressed the consequences of those phosphorylations on the interactions of p53, Mdm2, and Mdmx, and the consequent control of p53 transcriptional activity. The results suggest that MdmX may amplify or stabilize DNA damage-induced p53 responses and that the effects of MdmX are mediated by accumulated reservoirs of p53:MdmX and Mdm2:MdmX heterodimers. A survey of kinetic parameter space disclosed regions of switch-like behavior stemming from such reservoir-based transients. During an early response to DNA damage, MdmX positively or negatively regulated p53 activity, depending on the level of Mdm2, and led to amplification of p53 activity and switch-like response. During a late response to DNA damage, MdmX was observed to dampen oscillations of p53 activity. A possible role of MdmX therefore may be to dampen p53 oscillations that otherwise could produce erratic cell behavior or cell death. We think that metabolic oscillation could make cells vulnerable to catastrophic transients, and that control networks may be designed to dampen such oscillations. Oscillations of p53 could be particularly hazardous, because a transient swing might trigger inappropriate cell death. The ability of MdmX to dampen p53 oscillations in our simulations is therefore of interest. We have now initiated experiments to see whether such effects can be seen in cell cultures. prominently displayed in journal issues (such as being featured on the cover). MIMs however have not yet been widely used by others, presumably because of the effort required to learn the rules of the notation. Our colleagues have found the effort to be well worth while, in part because adhering to the rules of the MIM notation imposes a discipline of logic that enforces clear understanding. In order to help others adopt the notation, we published an article this year that more clearly defines the rules of the notation and that provides a systematic set of examples (Kohn et al. (2006) Mol. Biol. Cell 17: 1-13). The article can serve both as a reference manual and as a tutorial. By publishing this article in a major cell biology journal, we aimed for the broad cell biology community, rather than narrowly for bioinformaticists and systems biologists. The editors selected examples of our molecular interaction maps for display on the journal cover in order to bring our method to the attention of a wide audience of cell biologists. Relevant to bioinformatics and systems biology, we prepared another article (Kohn et al. (2006) Mol. Syst. Biol., in press), in which we address two major issues. First, we show the advantages of the MIM notation relative to other proposed notations. Second, we show how the MIM notation can represent networks for combinatorial simulation, a capability not shared by any other proposed notation. To demonstrate this point, we prepared a concise representation of a combinatorial simulation of the core of epidermal growth factor signaling recently published by Blinov et al, consisting of over 3000 individual reactions. This insight then led to a collaboration with Michael Blinov et al, in which we plan to mesh our MIM notation with their combinatorial network program. On the computation front this year, our group began a simulation study of the role of Mdmx in the response of p53 to DNA damage. In collaboration with Dr. Mirit Aladjem in our Laboratory, we plan to link simulation results with experiments.

关于控制细胞分裂周期和细胞凋亡的分子成分的大量信息已经积累起来，这些信息对开发新的抗癌疗法至关重要。然而，相关的分子相互作用网络是如此复杂，以至于很难理解它们是如何起作用的。因此，使用标准化符号的综合网络图就像电路图在电子学中必不可少一样，是必不可少的。然而，由于多分子复合物和修饰偶然性所起的重要作用，绘制生物调节网络的图表提出了一个重大挑战。几年前，我们为分子相互作用图（MIM）设计了一种表示法，该领域的许多人现在认为这是详细表示网络的最佳方法。我们已经在许多出版物中使用了mim，并且已经在期刊中突出展示了一些例子（例如在封面上出现）。要生成MIM，必须遵守正式的MIM符号规则（在Kohn等人Mol. Biol中描述）。Cell 17:1-13, 2006)；这个要求强加了一个有用的逻辑规则，强制对有关网络结构的可用信息有清晰的理解。与其他提出的符号相比，MIM符号具有重要的优势，并且具有表示分子相互作用网络中组合复杂性的独特能力（Kohn等人Mol. Systems Biol.）。doi: 10.1038 / msb4100088, 2006)。为了方便地访问mim，我们将它们发布在互联网上，并附有注释、参考文献和其他数据库的链接（http://discover.nci.nih.gov/mim/）。在过去的一年里，我们首次展示了MIMs如何在DNA和染色质水平上代表网络事件。我们制备了一个MIM，描述了DNA复制叉前核小体拆卸、复制叉后组装以及表观遗传信息复制到复制的染色质上所涉及的网络。我们使用该MIM对这些事件中发生的分子相互作用进行了全面回顾，从而显示了以这种方式将信息系统化的优势（Kohn等人Mol. Biol）。细胞19:1- 7,2008)。我们还展示了MIM表示法如何描述停滞的复制分叉（准备中）中的事件。在后一种情况下，我们设计了一个分层程序来准备有序的mim，克服了通常描述网络交互的随意方式。p53肿瘤抑制因子是DNA损伤反应的关键组成部分，DNA损伤反应可以阻止细胞周期的进展或导致细胞凋亡。这些反应在很大程度上是由p53作为一种转录因子的活性控制的，而p53主要通过与Mdm2和MdmX的相互作用来控制。虽然Mdm2对p53的控制作用已被充分了解，但Mdmx在该控制网络中的作用尚不清楚。因此，我们组装了包含p53控制网络的相互作用的更新MIM。从该MIM中，我们为计算机模拟提取了一个明确的模型网络，我们认为它可能是涉及Mdm2和MdmX的控制系统的核心。在模拟模型中，我们调查了p53活性的DNA损伤反应，作为控制Mdm2和MdmX的动力学参数的函数。我们用p53、Mdm2和MdmX的磷酸化速率来模拟DNA损伤。该模型表达了这些磷酸化对p53、Mdm2和Mdmx相互作用的影响，以及随后对p53转录活性的控制。结果表明，MdmX可能会放大或稳定DNA损伤诱导的p53反应，并且MdmX的作用是由p53:MdmX和Mdm2:MdmX异二聚体蓄积库介导的。对动力学参数空间的调查揭示了由这种基于储层的瞬变产生的开关行为区域。在对DNA损伤的早期反应中，MdmX根据Mdm2的水平正或负调节p53活性，并导致p53活性的扩增和开关样反应。在对DNA损伤的晚期反应中，MdmX被观察到抑制p53活性的振荡。因此，MdmX的一个可能作用可能是抑制p53振荡，否则p53振荡可能产生不稳定的细胞行为或细胞死亡。我们认为，代谢振荡可能使细胞容易受到灾难性瞬变的影响，而控制网络可能旨在抑制这种振荡。p53的波动可能特别危险，因为短暂的波动可能引发不适当的细胞死亡。因此，在我们的模拟中，MdmX抑制p53振荡的能力令人感兴趣。我们现在已经开始实验，看看这种效应是否可以在细胞培养中看到。在期刊的显著位置上展示（如在封面上）。然而，mim还没有被其他人广泛使用，可能是因为需要努力学习符号规则。我们的同事发现这种努力是值得的，部分原因是坚持MIM符号的规则强加了一种强制清晰理解的逻辑纪律。为了帮助其他人采用这种符号，我们今年发表了一篇文章，更清楚地定义了符号的规则，并提供了一套系统的例子(Kohn et al. (2006) Mol. Biol。细胞17:1 -13)。这篇文章既可以作为参考手册，也可以作为教程。通过在主要的细胞生物学杂志上发表这篇文章，我们的目标是广泛的细胞生物学社区，而不是狭隘的生物信息学家和系统生物学家。编辑们选择了我们的分子相互作用图的例子，以便在杂志封面上展示，以使我们的方法引起广泛的细胞生物学家的注意。与生物信息学和系统生物学相关，我们准备了另一篇文章(Kohn et al. (2006) Mol. Syst。医学杂志。（已出版），我们在其中讨论两个主要问题。首先，我们展示了相对于其他建议的表示法，MIM表示法的优点。其次，我们展示了MIM符号如何表示用于组合模拟的网络，这是任何其他提议的符号所不具有的功能。为了证明这一点，我们准备了Blinov等人最近发表的表皮生长因子信号传导核心组合模拟的简明表述，包括3000多个个体反应。这一见解随后导致了与Michael Blinov等人的合作，其中我们计划将我们的MIM符号与他们的组合网络程序相结合。在今年的计算方面，我们小组开始了Mdmx在p53对DNA损伤反应中的作用的模拟研究。我们计划与实验室的Mirit Aladjem博士合作，将模拟结果与实验联系起来。