Reverse engineering biological networks :applications in immune responses to bio-toxins.

Martino, Anthony A. ; Sinclair, Michael B. ; Davidson, George S. ; Haaland, David Michael ; Timlin, Jerilyn Ann ; Thomas, Edward Victor ; Slepoy, Alexander ; Zhang, Zhaoduo ; May, Elebeoba Eni ; Martin, Shawn Bryan ; Faulon, Jean-Loup Michel

Sandia National Laboratories

关键词: Network Analysis;    Phosphotransferases;    Genes;    59 Basic Biological Sciences;    Sampling;   

DOI  :  10.2172/877733
RP-ID  :  SAND2005-7989
RP-ID  :  AC04-94AL85000
RP-ID  :  877733

美国|英语

来源: UNT Digital Library

【 摘 要 】

Our aim is to determine the network of events, or the regulatory network, that defines an immune response to a bio-toxin. As a model system, we are studying T cell regulatory network triggered through tyrosine kinase receptor activation using a combination of pathway stimulation and time-series microarray experiments. Our approach is composed of five steps (1) microarray experiments and data error analysis, (2) data clustering, (3) data smoothing and discretization, (4) network reverse engineering, and (5) network dynamics analysis and fingerprint identification. The technological outcome of this study is a suite of experimental protocols and computational tools that reverse engineer regulatory networks provided gene expression data. The practical biological outcome of this work is an immune response fingerprint in terms of gene expression levels. Inferring regulatory networks from microarray data is a new field of investigation that is no more than five years old. To the best of our knowledge, this work is the first attempt that integrates experiments, error analyses, data clustering, inference, and network analysis to solve a practical problem. Our systematic approach of counting, enumeration, and sampling networks matching experimental data is new to the field of network reverse engineering. The resulting mathematical analyses and computational tools lead to new results on their own and should be useful to others who analyze and infer networks.