Software & Hardware Inference Engines for Automated Determination of Primary Cell

描述（由申请人提供）：后基因组生物学的一个核心问题是了解生物系统如何在其组成部分之间的相互作用中发挥作用，以及临床研究者如何希望与临床样本一起工作，并将机制理解与药物治疗选择和治疗结果联系起来？这些都是目前在临床环境中“护理点”的方法无法有效解决的关键问题。在此，我们建议四个研究实验室建立生物工程研究伙伴关系（BRP），将他们在癌症信号、系统生物学、建模、算法和硬件方面的专业知识结合起来，以充分发展这种方法，研究癌症内部的信号网络以及浸润性和全局免疫反应。诺兰实验室已经开发出在单细胞水平上同时测量多种蛋白质状态的能力。原则上，通过监测细胞系统在适当不同的扰动下的状态，可以通过计算重建指定系统所需的所有相互作用。利用多色流式细胞术，Nolan实验室通过重建正常人类外周血中参与t细胞信号转导的11种蛋白之间的相互作用，证明了这种方法的可行性（Sachs 2005）。然而，对大型网络的研究提出了网络推理的主要挑战，其解决方案需要从根本上采用新的算法和硬件设计策略。在这些研究中，我们将利用计算机硬件设计方面领先的创新进展，使用多重配置的现场可编程门阵列与高带宽内存连接（Teresa b孟，斯坦福大学和John Wawrzynek（加州大学伯克利分校）），并结合统计理论的进展（Wing Wong，斯坦福大学）。这些硬件和软件实现将应用于从正常人类和小鼠样本集收集的数据集，以建立“正常”基线，并作为我们研究的控制。细胞类型特异性子网络的演示本身将是整个研究界的宝贵资源，并将代表人类和小鼠正常免疫系统中第一个全球信号数据库。我们将使用两种癌症系统将该数据库与癌症肿瘤浸润细胞中发生的免疫系统变化进行对比。

DESCRIPTION (provided by applicant): A central problem in post-genome biology is to understand how a biological system functions in terms of the interactions among its components and how do clinical investigators hope to work with clinical samples and relate mechanistic understandings to drug treatment choice & therapeutic outcomes? These are critical issues that are not effectively addressed by current approaches at the "point of care" in the clinical setting. We propose here a Bioengineering Research Partnership (BRP) for four research labs to combine their exper-tise in cancer signaling, systems biology, modeling, algorithm and hardware, to fully develop this approach for the study of signaling networks within cancer and the infiltrating and global immune response. The Nolan lab has developed the ability to measure the status of multiple proteins simultaneously at the single cell level. In principle, by mon-itoring the status of a cellular system under a suitably diverse set of perturbations, one can computationally reconstruct all the interactions needed to specify the system. Using polychromatic flow cytometry, the Nolan laboratory has demonstrated the feasibility of this approach by reconstructing the interactions among 11 pro-teins involved in T-cell signal transduction from normal human peripheral blood (Sachs 2005). The study of larger networks, however, raises major challenges in network inference whose resolutions require fundamentally new strategies for algorithmic and hardware design. We will utilize leading, innovative advances in com-puter hardware design using multiply configured Field Programmable Gate Arrays coupled to high bandwidth memory connections (Teresa Meng, Stanford and John Wawrzynek (UC Berkeley), in conjunction with advances in statistical theory (Wing Wong, Stanford) for these studies. These hardware and software implementations will be applied to datasets collected from normal human and murine mouse sample sets to establish a baseline of 'normality' and as control for our studies. The demonstration of cell type specific sub-networks by itself will be an invaluable resource for the entire research community and will represent the first ever global database of signaling in normal immune systems of humans and mice. We will contrast this database with the immune system changes that occur in cancer tumour infiltrating cells using two cancer systems.