BUILDING CELL TYPE-SPECIFIC SIGNALING MODELS IN BREAST CANCER CELL LINES

这个子项目是利用资源的许多研究子项目之一。 由NIH/NCRR资助的中心拨款提供。对子项目的主要支持 子项目的首席调查员可能是由其他来源提供的， 包括美国国立卫生研究院的其他来源。为子项目列出的总成本可能 表示该子项目使用的中心基础设施的估计数量， 不是由NCRR赠款提供给次级项目或次级项目工作人员的直接资金。 信号转导通路的数学模型被用来理解生长因子受体的作用，预测暴露于治疗药物的后果，并设计出最佳的治疗方法。到目前为止构建的生物化学上现实的模型受到这样一个事实的影响，即关键信号蛋白的浓度几乎没有通过实验确定。因此，浓度必须通过计算进行估计或拟合，这极大地限制了创建准确的预测性模型的能力。 为了确定人乳腺癌细胞系中信号蛋白的相对浓度，我们将应用一种新的质谱学技术。蛋白质数据将与转录图谱数据进行比较，使我们能够更好地理解mRNA和蛋白质水平之间的联系。选定的蛋白质和转录本也将使用Western blotting和qPCR进行检测，而重组蛋白将作为绝对标准。然后，测量的蛋白质和信使核糖核酸水平将被用来为动力学模型提供信息，具体目标是解释细胞类型的特定信号动力学。 拟议的试点研究将有助于两个相关领域。对于蛋白质组学来说，能够测量多种蛋白质的浓度并了解信使核糖核酸和蛋白质水平之间的相关性将是一个强大的工具。它将使细胞系、组织和原代细胞具有更广泛的蛋白质组学特征，并揭示mRNA阵列和qPCR实验是否有助于理解基于蛋白质的机制。对于建模，对蛋白质组进行量化将使我们能够减少自由参数的数量，提高模型的可识别性。完整的蛋白质组学特征可能使仅根据蛋白质水平的可变性来预测不同细胞的行为成为可能。综上所述，这些发展应该会加速我们预测药物作用效果的能力，并最终实现个性化治疗方案。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Mathematical modeling of signal transduction pathways is used to understand the actions of growth factor receptors, to predict the consequences of exposure to therapeutic drugs, and to devise optimal treatments. Biochemically realistic models constructed to date suffer from the fact that few if any of the concentrations of key signaling proteins have been determined experimentally. Thus, concentrations must be estimated or fitted computationally, greatly limiting the ability to create accurate, predictive models. To determine the relative concentrations of signaling proteins in human breast cancer cell lines we will apply a novel mass spectrometry technique. Protein data will be compared to data from transcription profiles, enabling us to better understand the connection between mRNA and protein levels. Selected proteins and transcripts will also be measured using Western blotting and qPCR, while recombinant proteins will serve as absolute standards. Measured protein and mRNA levels will then be used to inform kinetic models with the specific goal of explaining cell-type specific signaling dynamics. The proposed pilot study will contribute to two related fields. For proteomics, the ability to measure the concentrations of multiple proteins and to understand the correlation between mRNA and protein levels will be a powerful tool. It will enable more general proteomic characterization of cell lines, tissues and primary cells, and reveal whether mRNA arrays and qPCR experiments are useful for understanding protein-based mechanisms. For modeling, quantifying the proteome will enable us decrease the number of free parameters and improve model identifiability. A full proteomic characterization might make it possible to predict behaviors in different cells on the basis of variability in protein levels alone. Taken together, these developments should accelerate our ability to predict the efficacy of drug action and ultimately to personalize therapeutic protocols.