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.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA130826-02
Application #
7694305
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Li, Jerry
Project Start
2008-09-26
Project End
2012-07-31
Budget Start
2009-09-01
Budget End
2010-07-31
Support Year
2
Fiscal Year
2009
Total Cost
$1,676,305
Indirect Cost
Name
Stanford University
Department
Biology
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
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