Contemporary systems biology is shifting the paradigm of biomedical research from minimalistic studies of individual genes/proteins to integration of information at systems level. Current high throughput biotechnologies enable collection of a large amount of biological information, and the different aspects of the cellular systems are reflected with heterogeneous data, e.g., genomics, epigenomics, transcriptomics and metabolomics. However, it remains a major challenge to systematically integrate this body of information and derive biological insights at a mechanistic level. The overarching goal of this project is to develop a computational system that enables integration of various high throughput """"""""omics"""""""" data (an """"""""integromics"""""""" approach) to gain insights into cellular systems, in particular the signal transduction systems. The activities of the project are organized into four specific aims, which progress from approaches for capturing general information among the multiple omics data to more specific and complex models designed to decipher specific cellular signaling systems. Firstly, we will develop a general framework, based on information theory and probabilistic models, to identify information modules that convey biological information between different """"""""omics"""""""" data at large scale. Secondly, we develop methods to further investigate if the information from the multiple omics data reflects causal relationships. Thirdly, we will develop tools to recover missing information from the system to augment the high throughput technologies. Finally, we will develop a unified model to elucidate signal transduction pathways by integrating information form multiple omics data in manner that is both biologically sensible and mathematically rigorous. We expect that the methodologies developed in the project are widely applicable to study a variety of cellular signal transduction systems.

Public Health Relevance

Cellular signaling systems play critical roles in terms of maintaining the normal physiology environment for cells, organs and human body;many human diseases, e.g., cancers and AIDS, are resulted from the disrupted cellular signaling systems. Investigating of cellular signaling systems will not only help to understand the mechanisms of disease but will also facilitate the discoveries of treatments. This project aim to develop novel computational methods to integrate the information regarding different aspects of cellular signaling systems in a biologically sensible and mathematically principled manner, which will lead both novel computational tools and biological discoveries.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Special Emphasis Panel (ZLM1-ZH-C (M3))
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Ye, Jane
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University of Pittsburgh
Schools of Medicine
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Chen, Lujia; Cai, Chunhui; Chen, Vicky et al. (2016) Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. BMC Bioinformatics 17 Suppl 1:9
Lu, Songjian; Cai, Chunhui; Yan, Gonghong et al. (2016) Signal-Oriented Pathway Analyses Reveal a Signaling Complex as a Synthetic Lethal Target for p53 Mutations. Cancer Res 76:6785-6794
Day, Roger S (2016) Planning clinically relevant biomarker validation studies using the ""number needed to treat"" concept. J Transl Med 14:117
Lu, Songjian; Mandava, Gunasheil; Yan, Gaibo et al. (2016) An exact algorithm for finding cancer driver somatic genome alterations: the weighted mutually exclusive maximum set cover problem. Algorithms Mol Biol 11:11
Lu, Songjian; Lu, Kevin N; Cheng, Shi-Yuan et al. (2015) Identifying Driver Genomic Alterations in Cancers by Searching Minimum-Weight, Mutually Exclusive Sets. PLoS Comput Biol 11:e1004257
Chen, Lujia; Cai, Chunhui; Chen, Vicky et al. (2015) Trans-species learning of cellular signaling systems with bimodal deep belief networks. Bioinformatics 31:3008-15
Cai, Chunhui; Chen, Lujia; Jiang, Xia et al. (2014) Modeling signal transduction from protein phosphorylation to gene expression. Cancer Inform 13:59-67
Mowrey, David; Cheng, Mary Hongying; Liu, Lu Tian et al. (2013) Asymmetric ligand binding facilitates conformational transitions in pentameric ligand-gated ion channels. J Am Chem Soc 135:2172-80
Lu, Songjian; Jin, Bo; Cowart, L Ashley et al. (2013) From data towards knowledge: revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data. PLoS One 8:e61134
Day, Roger S; McDade, Kevin K (2013) A decision theory paradigm for evaluating identifier mapping and filtering methods using data integration. BMC Bioinformatics 14:223

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