A logical model provides insights into T cell receptor signaling.
A logical model provides insights into T cell receptor signaling.
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DOI：
10.1371/journal.pcbi.0030163
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发表时间：
2007-08
影响因子：
4.3
通讯作者：
Schraven B
中科院分区：
文献类型：
--
作者：
Saez-Rodriguez J;Simeoni L;Lindquist JA;Hemenway R;Bommhardt U;Arndt B;Haus UU;Weismantel R;Gilles ED;Klamt S;Schraven B
细胞决策是由复杂的分子相互作用网络决定的。目前正在重建大规模信号网络，但用于动态建模的动力学参数和定量数据仍然稀缺。因此，基于这些网络结构的计算研究引起了极大的兴趣。在这里，依赖于逻辑形式主义的方法被应用于通过 T 细胞受体、CD4/CD8 共受体和辅助信号受体 CD28 控制 T 细胞激活的复杂信号网络的功能分析。我们的大规模布尔模型由 94 个节点和 123 个相互作用组成，基于来自原代 T 细胞的成熟定性知识，揭示了重要的结构特征（例如反馈循环和网络范围的依赖性），并概括了该网络的全局行为，以获取有关野生型和敲除条件下 T 细胞激活的一系列已发表数据。更重要的是，该模型预测了抗体介导的 CD28 扰动和激酶 Fyn 基因敲除后出现的意外信号事件，随后经过实验验证。最后，我们表明逻辑模型揭示了网络功能中的关键要素和潜在的故障模式，并为缺失的链接提供了候选者。总之，我们的 T 细胞激活大规模逻辑模型被证明是一种很有前途的计算机工具，它激发免疫学家提出新问题。我们认为它在预测药物和网络修改的影响方面具有宝贵的潜力。 T 淋巴细胞是适应性免疫反应的核心调节因子，它们的不当激活可能导致自身免疫性疾病或癌症。了解 T 细胞激活的信号传导机制是制定药物干预和疾病治疗新策略的先决条件。然而，许多关于 T 细胞信号传导的现有文献与 T 细胞发育或转化 T 细胞系（例如 Jurkat）的激活过程有关，而有关非转化原代 T 细胞的信息有限。在这里，免疫学家和理论家从现有文献中收集了源自原代 T 细胞分析的数据。他们利用这些信息建立了一个定性布尔网络，描述 TCR、CD4/CD8 共受体和 CD28 参与后的 T 细胞激活机制。该网络由 94 个节点组成，可以扩展以方便解释 T 细胞激活实验分析中出现的新数据。新开发的工具和方法允许进行计算机分析和网络操纵，并可以发现隐藏/不可预见的信号通路。事实上，通过评估 CD28 和蛋白酪氨酸激酶 Fyn 控制的信号事件，我们表明即使是定性网络的计算分析也可以提供新的、非明显的信号通路，这些通路可以通过实验进行验证。
Cellular decisions are determined by complex molecular interaction networks. Large-scale signaling networks are currently being reconstructed, but the kinetic parameters and quantitative data that would allow for dynamic modeling are still scarce. Therefore, computational studies based upon the structure of these networks are of great interest. Here, a methodology relying on a logical formalism is applied to the functional analysis of the complex signaling network governing the activation of T cells via the T cell receptor, the CD4/CD8 co-receptors, and the accessory signaling receptor CD28. Our large-scale Boolean model, which comprises 94 nodes and 123 interactions and is based upon well-established qualitative knowledge from primary T cells, reveals important structural features (e.g., feedback loops and network-wide dependencies) and recapitulates the global behavior of this network for an array of published data on T cell activation in wild-type and knock-out conditions. More importantly, the model predicted unexpected signaling events after antibody-mediated perturbation of CD28 and after genetic knockout of the kinase Fyn that were subsequently experimentally validated. Finally, we show that the logical model reveals key elements and potential failure modes in network functioning and provides candidates for missing links. In summary, our large-scale logical model for T cell activation proved to be a promising in silico tool, and it inspires immunologists to ask new questions. We think that it holds valuable potential in foreseeing the effects of drugs and network modifications. T-lymphocytes are central regulators of the adaptive immune response, and their inappropriate activation can cause autoimmune diseases or cancer. The understanding of the signaling mechanisms underlying T cell activation is a prerequisite to develop new strategies for pharmacological intervention and disease treatments. However, much of the existing literature on T cell signaling is related to T cell development or to activation processes in transformed T cell lines (e.g., Jurkat), whereas information on non-transformed primary T cells is limited. Here, immunologists and theoreticians have compiled data from the existing literature that stem from analysis of primary T cells. They used this information to establish a qualitative Boolean network that describes T cell activation mechanisms after engagement of the TCR, the CD4/CD8 co-receptors, and CD28. The network comprises 94 nodes and can be extended to facilitate interpretation of new data that emerge from experimental analysis of T cell activation. Newly developed tools and methods allow in silico analysis, and manipulation of the network and can uncover hidden/unforeseen signaling pathways. Indeed, by assessing signaling events controlled by CD28 and the protein tyrosine kinase Fyn, we show that computational analysis of even a qualitative network can provide new and non-obvious signaling pathways which can be validated experimentally.
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影响因子：
64.8
作者：
Alon, U;Surette, MG;Leibler, S
通讯作者：
Leibler, S
DOI：
10.1084/jem.190.3.375
发表时间：
1999-08-02
期刊：
The Journal of experimental medicine
影响因子：
--
作者：
Holdorf AD;Green JM;Levin SD;Denny MF;Straus DB;Link V;Changelian PS;Allen PM;Shaw AS
通讯作者：
Shaw AS
影响因子：
5.3
作者：
Arnold, R;Patzak, IM;Kiefer, F
通讯作者：
Kiefer, F
影响因子：
56.9
作者：
Lee, KH;Holdorf, AD;Shaw, AS
通讯作者：
Shaw, AS
影响因子：
4.4
作者：
Kovacs, B;Parry, RV;Finkel, TH
通讯作者：
Finkel, TH