There is an urgent need to develop systematic platforms to address the challenges and opportunities brought forth by the fast progresses ofthe 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 ofthe 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 ofthe 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 ofinterest. XML-based protocols will be used for data exchanging.
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.
|Peng, Huiming; Peng, Tao; Wen, Jianguo et al. (2014) Characterization of p38 MAPK isoforms for drug resistance study using systems biology approach. Bioinformatics 30:1899-907|
|Ji, Zhiwei; Su, Jing; Liu, Chenglin et al. (2014) Integrating genomics and proteomics data to predict drug effects using binary linear programming. PLoS One 9:e102798|
|Peng, Tao; Peng, Huiming; Choi, Dong Soon et al. (2014) Modeling cell-cell interactions in regulating multiple myeloma initiating cell fate. IEEE J Biomed Health Inform 18:484-91|
|Zhang, Rengjing; Zhao, Chen; Xiong, Zixiang et al. (2014) Pathway bridge based multiobjective optimization approach for lurking pathway prediction. Biomed Res Int 2014:351095|
|Wang, Hongyan; Zhou, Xiaobo (2013) Detection and characterization of regulatory elements using probabilistic conditional random field and hidden Markov models. Chin J Cancer 32:186-94|
|Shao, Hongwei; He, Ying; Li, King C P et al. (2013) A system mathematical model of a cell-cell communication network in amyotrophic lateral sclerosis. Mol Biosyst 9:398-406|
|Sun, Xiaoqiang; Kang, Yunqing; Bao, Jiguang et al. (2013) Modeling vascularized bone regeneration within a porous biodegradable CaP scaffold loaded with growth factors. Biomaterials 34:4971-81|
|Tan, Hua; Wei, Kun; Bao, Jiguang et al. (2013) In silico study on multidrug resistance conferred by I223R/H275Y double mutant neuraminidase. Mol Biosyst 9:2764-74|
|Bian, Jiawen; Liu, Chenglin; Wang, Hongyan et al. (2013) SNVHMM: predicting single nucleotide variants from next generation sequencing. BMC Bioinformatics 14:225|
|Fan, Jing; Dy, Jennifer G; Chang, Chung-Che et al. (2013) Identification of SNP-containing regulatory motifs in the myelodysplastic syndromes model using SNP arrays and gene expression arrays. Chin J Cancer 32:170-85|
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