Deconvoluting Ras Signaling Networks in T Cell Lymphoma

异常信号事件在癌症和其他疾病中起着核心作用。因此，阐明信号网络的结构对于理解正常和恶性生长至关重要。由于信号传递过程涉及许多相互作用的成分，这些成分共同作用，使功能相关的集体现象出现， 通常很难从实验观察中凭直觉理解机械原理。进一步混淆直觉的是相关过程固有的随机性。最近对原代细胞和特征明确的细胞系的研究为生物信号网络的复杂性和异质性提供了生动的例子。我们最近的研究 还说明了如何互补的理论和实验研究可以帮助阐明淋巴细胞中Ras信号的复杂特征。该项目的中心主题是在物理科学和生命科学的交叉点采用这种方法，以解卷积异常Ras信号传导的起源及其在环境中的后果。 特异性T细胞淋巴瘤。我们希望我们的发现对各种癌症有广泛的影响。 这一目标将通过以下具体目标来实现：（目标1）扩展受体诱导的计算模型， 正常T淋巴细胞中的Ras活化包括下游RAF/MEK/ERK-、PIS激酶/Akt/mTOR/S6激酶-和RalGDS-效应子途径，以及这些途径之间的串扰。模型预测将被用来设计实验，可以区分不同的假设之间的敏感。然后我们将在 实验和计算研究。(Aim 2）通过测试针对正常和致癌Ras突变体以及致癌RasGRPI T细胞淋巴瘤模型的计算预测来进一步发展模型。将遵循计算和实验研究之间的迭代范式。分析复杂合作的效果 遗传损伤(Aim 3）使用计算模型来定义网络内的关键致癌节点，并通过化学抑制剂、shRNA和PI 3激酶或RAF激活缺陷的新型Kras等位基因实验性地测试预测的淋巴瘤的脆弱性。

Aberrant signaling events play a central role in cancer and other diseases. Elucidating the architecture of signaling networks is therefore essential for understanding both normal and malignant growth. Because signaling processes involve many interacting components that act in concert for functionally-relevant collective phenomena to emerge, it is often difficult to intuit mechanistic principles from experimental observations. Further confounding intuition is the inherently stochastic character of the pertinent processes. Recent studies of primary cells and well-characterized cell lines provide vivid examples of such complexity and heterogeneity of biological signaling networks. Our recent studies also illustrate how complementary theoretical and experimental studies can help elucidate complex features of Ras signaling in lymphocytes. The central theme of this project is to employ such an approach at the crossroads of the physical and life sciences to deconvolute the origins of aberrant Ras signaling and its consequences in the context of a specific T cell lymphoma observed in the clinic. We ex pect our findings to have broad implications for diverse cancers. This goal will be achieved by pursuing the following specific aims: (Aim 1) To extend computational models of receptorinduced Ras activation in normal T lymphocytes to include the downstream RAF/MEK/ERK-.PISkinase/Akt/mTOR/S6klnase-, and RalGDS-effector pathways, and cross-talk between these pathways. Model predictions will be used to design experiments that can discriminate sensitively between different hypotheses. We will then iterate between experiments and computational studies. (Aim 2) To develop the models further by testing computational predictions against normal and oncogenic Ras mutant as well as oncogenic RasGRPI T cell lymphoma models. The paradigm of iteration between computational and experimental studies will be followed. Analyze the effects of complex cooperating genetic lesions. (Aim 3) To use computational models to define critical oncogenic nodes within the networks and test the predicted lymphoma's vulnerability experimentally via chemical inhibitors, shRNA, and novel Kras alleles that are defective for PI3 kinase or RAF activation.