Combining Computational and Experimentation to Interrogate NF-kappaB Signaling

描述（由申请人提供）：本研究的目的是利用系统生物学方法来了解控制转录因子 NF-kappaB 激活的特异性和时间机制，以及 NF-kappaB 如何引发转录反应，以便更好地了解某些类型癌症的进展。基于系统和计算的方法特别适合解决这个问题。 使用组合的计算模型/实验方法，我们能够表征从 LPS 刺激到 NF-kappaB 激活的途径中以前未知的部分。这种方法还解释了一种复杂的行为：在 LPS 刺激下观察到 NF-κB 的稳定激活。展望未来，我们建议继续使用集成方法在多个抽象级别上研究 NF-kappaB 网络。我们的具体目标是：（1）重建和分析整个已知的 NF-kappaB 信号传导和转录网络的网络模型； (2) 得出对 NF-κB 激活的基因表达反应的详细描述和模型； (3) 将B细胞相关的NF-kappaB激活动力学的定量描述构建成详细的计算模型； (4) 观察和模拟单细胞中 NF-κB 相关的激活和自分泌/旁分泌信号传导。 通俗语言摘要：我们构建计算机模型以及使用模型预测指导实验的能力的进步，在帮助我们了解癌症进展方面具有巨大的潜力。我们建议使用计算机建模和实验来帮助我们了解 NF-kappaB 信号网络及其在癌症发展中的作用，特别是在弥漫性大 B 细胞淋巴瘤方面。

DESCRIPTION (provided by applicant): The purpose of this research is to use systems biology approaches to understand the specificity and temporal mechanisms that govern activation of transcription factor NF-kappaB, as well as how NF-kappaB evokes a transcriptional response, in order to better understand progression of certain types of cancer. Systems-based and computational approaches are particularly well-suited to address this issue. Using a combined computational modeling/experimental approach, we were able to characterize a previously-unknown part of the pathways which led from LPS stimulation to NF-kappaB activation. This approach also led to the explanation of a complex behavior: the observed stable activation of NF-kappaB under LPS stimulation. Moving forward, we propose to continue using an integrated approach to study the NF-kappaB network at several levels of abstraction. Our Specific Aims are to: (1) reconstruct and analyze a network model of the entire known NF-kappaB signaling and transcriptional network; (2) derive a detailed description and model of the gene expression response to NF-kappaB activation; (3) build a quantitative description of B cell-related NF-kappaB activation dynamics into a detailed computational model; and (4) observe and model NF-kappaB-related activation and autocrine/paracrine signaling in single cells. PLAIN LANGUAGE SUMMARY: Advances in our ability to build computer models, and to use model predictions to guide experiments, have great potential in helping us to understand cancer progression. We propose to use computer modeling and experiments to help us understand the NF-kappaB signaling network and its role in cancer development, particularly with regard to diffuse large B cell lymphomas.