There is an urgent need to develop systematic platforms to address the challenges and opportunities brought forth by the fast progresses of the Library of Integrated Network-Based Cellular Signatures (LINCS) program. The LINCS program performs cross-cutting high-throughput assays and develops integrative computational analysis of informative molecular activity and cellular feature signatures generated in response to a variety of perturbing agents and drug candidates. The primary goal of the proposed study is to address the needs by developing a signature-oriented software platform, the Integrative and Translational Network-based Cellular Signature Analyzer (itNETZ). The working flow of the system is: 1) to identify disease- and drug-specific molecular and cellular features, 2) to reveal the mechanismistic associations between components of such features and delineate them as signaling and regulating networks, 3) to present the dynamics of such networks by mathematical models, 4) to construct network-based molecular and cellular signatures, 5) to discover common signatures and networks across cell lines and diseases, 6) to establish and maintain a public resource of the increasing resultant knowledge of therapeutic responses, and 7) to facilitate the research community on querying signatures of interests, exploring correlations among signatures, and generating hypotheses. This system will enable the following functions: first, the basic analysis and discovery toolkits for processing cellular images, Luminex genomics data, transcriptome sequencing data, and for modeling phosphoproteins signaling pathway;and second, data integration and mining toolkits for mapping genomics and proteomics to cellular phenotypes, for core pathway signature identification on cell lines treated by different inhibitors and drug-induced pathway signature alterations, and for constructing drug kinome landscapes. The itNETZ system comprises pipelines that load input, analyze images, process genomics and proteomics data, export outputs into a relational database, integrate and mine the data, and generate network-based cellular signatures of interest. XML-based protocols will be used for data exchanging.

Public Health Relevance

This project will be a substantial contribution to the public health by understanding the mechanism of drugs, and network signature under different treatment conditions. More importantly, the completion of this project will help to answer some critical questions related to drug target signatures. Such understanding will in turn advance our knowledge in tumor biology and open up the possibility of novel treatments in the future.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01HL111560-01
Application #
8231114
Study Section
Special Emphasis Panel (ZRG1-BST-H (55))
Program Officer
Larkin, Jennie E
Project Start
2011-09-24
Project End
2013-06-30
Budget Start
2011-09-24
Budget End
2012-06-30
Support Year
1
Fiscal Year
2011
Total Cost
$388,750
Indirect Cost
Name
Methodist Hospital Research Institute
Department
Type
DUNS #
185641052
City
Houston
State
TX
Country
United States
Zip Code
77030
Tan, Hua; Zhou, Xiaobo (2018) Detection of Combinatorial Mutational Patterns in Human Cancer Genomes by Exclusivity Analysis. Methods Mol Biol 1711:3-11
Tan, Hua; Chen, Ruoying; Li, Wenyang et al. (2017) A systems biology approach to studying the molecular mechanisms of osteoblastic differentiation under cytokine combination treatment. NPJ Regen Med 2:5
Wu, Dan; Yang, Xufang; Peng, Huiming et al. (2017) OCIAD2 suppressed tumor growth and invasion via AKT pathway in Hepatocelluar carcinoma. Carcinogenesis 38:910-919
Liu, Keqin; Beck, Dominik; Thoms, Julie A I et al. (2017) Annotating function to differentially expressed LincRNAs in myelodysplastic syndrome using a network-based method. Bioinformatics 33:2622-2630
Tan, Hua; Yi, Hualin; Zhao, Weiling et al. (2016) Intraglomerular crosstalk elaborately regulates podocyte injury and repair in diabetic patients: insights from a 3D multiscale modeling study. Oncotarget 7:73130-73146
Wang, Yongcui; Zhao, Weiling; Zhou, Xiaobo (2016) Matrix factorization reveals aging-specific co-expression gene modules in the fat and muscle tissues in nonhuman primates. Sci Rep 6:34335
Peng, Huiming; Tan, Hua; Zhao, Weiling et al. (2016) Computational systems biology in cancer brain metastasis. Front Biosci (Schol Ed) 8:169-86
Chen, Huaidong; Chen, Wei; Liu, Chenglin et al. (2016) Relational Network for Knowledge Discovery through Heterogeneous Biomedical and Clinical Features. Sci Rep 6:29915
Peng, Huiming; Zhao, Weiling; Tan, Hua et al. (2016) Prediction of treatment efficacy for prostate cancer using a mathematical model. Sci Rep 6:21599
Tan, Hua; Bao, Jiguang; Zhou, Xiaobo (2015) Genome-wide mutational spectra analysis reveals significant cancer-specific heterogeneity. Sci Rep 5:12566

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