Comparative Analysis of Information Transfer in NF-kappaB and MAPK Pathways

项目摘要 在活细胞中通过专门的分子通路和网络进行的信号转导受到大量 可变性。因此，信令输入的检测精度不是绝对的，从而限制了 传递的信息，从而信号条件下的表型结果的光谱。在上一次 ，我们开发了一个新的框架，用于分析噪声信令网络中的信息传输。 并将其应用于各种网络，最著名的是肿瘤坏死因子(TNF)刺激的 不同的靶点，包括最突出的核因子-kappaB转录因子。我们还建立了一个 该信令网络的逐步实验验证的确定性模型，该模型已经 广泛用于分析先天免疫反应，以及开发和验证 多样化的系统生物学工具。为了能够对信令网络进行高吞吐量分析，我们还 开发了一些新的微加工工具和简化的高通量单细胞分析。 尽管这项工作和许多其他实验室的工作在揭示对 肿瘤坏死因子激活的信号网络和许多其他真核信号通路和网络的工作原理， 还有很多东西有待探索。首先，我们仍然缺乏对可变性(噪声)是如何的完全了解 分布在复杂的多节点信令网络中。了解这样的噪声分布可以帮助 美国培训更现实的信令过程随机数学模型，并更好地理解 通过复杂的生化“通道”传递信息的机制。第二，在许多情况下， 尤其是肿瘤坏死因子诱导的信号过程，经常在模型细胞系中进行分析，通常 没有与表型输出(细胞决策)的明确联系。因此，目前还不清楚有多少 通过信令网络传输的信息用于通知小区决策。第三，分析了 信令过程主要通过施加简单化的输入来执行，而不是重构信令 可由传递肿瘤坏死因子和其他基于配体的信号的活细胞产生的输入。因此，在 在细胞培养实验中，我们可能不会研究对最相关或最有信息量的输入的反应。在这 应用，我们提出了一套建模和实验研究，以应对这些挑战，通过关注 更相关的细胞类型：巨噬细胞和内皮细胞，以及肿瘤坏死因子的相关表型结果 信号传递过程：内皮层通透性增加。我们建议建立详细的随机 分析这些细胞类型中的肿瘤坏死因子信号，并用它来描述异型细胞间的通讯。一个结果 这项分析的一套工具将分析内毒素(LSP)和肿瘤坏死因子信号对血管内皮细胞生长的影响 先天免疫反应的重要方面，内皮层完整性控制导致单核细胞 招募，这也将普遍适用于其他信令和细胞间通信系统。

Project Summary Signal transduction through dedicated molecular pathways and networks in live cells is subject to substantial variability. Thus the precision of detection of the signaling inputs is not absolute, limiting the amount of transmitted information and thus the spectrum of signal-conditioned phenotypic outcomes. In the previous funding periods, we developed a new framework for analysis of information transfer in noisy signaling networks and applied it to various networks, most notably the tumor necrosis factor (TNF) stimulated activation of diverse targets, including, most prominently the NF-kappaB transcription factor. We have also built a progressively experimentally validated deterministic model of this signaling network, which has been extensively used both in the analysis of the innate immune response and in development and validation of diverse systems biology tools. To enable high-throughput analysis of signaling networks, we have also developed a number of novel micro-fabricated tools and streamlined high throughput single cell analysis. Although this work and the work of many other labs have done much to uncover multiple insights into the workings of the TNF-activated signaling networks and many other eukaryotic signaling pathways and networks, much remains to be explored. First, we still lack complete understanding of how the variability (noise) is distributed across complex multi-node signaling networks. Knowledge of such noise distributions can both help us train more realistic stochastic mathematical models of the signaling processes and better understand the mechanisms of information transfer through complex biochemical `channels'. Second, in many cases, an in TNF induced signaling processes in particular, analysis is frequently performed in model cell lines, often without a clear connection to phenotypic outputs (the cell decision making). Thus it is not clear how much of the information transferred through signaling networks is used to inform cell decisions. Third, the analysis of signaling processes is mostly performed by imposing simplistic inputs, rather than reconstituting the signaling inputs that can be generated by live cells communicating TNF and other ligand-based signals. Thus, in experiments in cell culture, we might not study the response to the most relevant or informative inputs. In this application, we propose a set of modeling and experimental studies to address these challenges, by focusing on more relevant cell types: macrophages and endothelial cells, and the relevant phenotypic outcome of TNF signaling process: an increase in the endothelial layer permeability. We propose to build detailed stochastic analysis of TNF signaling in these cell types and use it to describe heterotypic cell-cell communication. A result of this analysis will be a set of tools to analyze the effect of lipopolysaccharide (LSP) and TNF signaling on an important aspect of the innate immune response, the endothelial layer integrity control leading to monocyte recruitment, which will also be universally applicable to other signaling and cell-cell communication systems.