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Pathway Analysis Using Protein Expression Data for Enhanced Biochemical Understanding

This project explores the correlation between protein expression profiles and biochemical pathways. Using advanced technologies like mass spectrometry and protein array technology, we aim to identify the most probable metabolic and regulatory pathways associated with specific proteins. By implementing an online service, users can predict and rank pathways based on activity and co-regulation scores. Our application integrates with the KEGG database, offering vital insights into cellular mechanisms, potential drug targets, and deeper biological understanding.

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Pathway Analysis Using Protein Expression Data for Enhanced Biochemical Understanding

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  1. Pathways Analysis using Protein Expression Data Venkatesh Jitender (vjk@cbmi.upmc.edu) Dr. Vanathi Gopalakrishnan Center for Biomedical Informatics, UPMC

  2. Bio-Chemical Pathways & Protein Expression Profiles • Organisms function through intricate networks of chemical reactions and interacting molecules. • Metabolic Pathway • Can be thought of as a “state representation” network • Regulatory Pathway: • Can be thought of as a “switch activating or de-activating” diagram Protein Expression Profiles: • Mass- spectrometry • Protein Array technology, etc.

  3. Suite of Databases and Associated Software http://www.genome.ad.jp/kegg/kegg2.html#pathway Includes : PATHWAY database GENES & PROTEINS (Genes / SSD / KO databases) CHEMICAL COMPOUNDS & REACTIONS (Compound / Reaction database) Statistics: Number of pathways10,677(PATHWAY database) Number of reference pathways226(PATHWAY database) KEGG Database:

  4. Metabolic Pathway:

  5. Regulatory Pathway:

  6. Proposal: • Identify co- relation between observed protein expression profiles and Biochemical Pathways. • Provide an online web-service to predict the “Most-Probable” pathway • Rank putative pathways based on Activity score: analyze difference in expression profiles Co regulation score: analyze similarity in expression profiles within a pathway Cascade score: analyze structure, measuring activity and coregulation

  7. Project Architecture Putative Pathway Set Application Server KEGG Server KEGG API Analysis Routine get_pathway_by_enzyme Recommended Pathway User submits sequence data

  8. Analysis Routine Application Server • Analysis Routine • Score Pathway based on: • Activity • Coregulation • Cascade Suggested Pathway Putative Pathway Set

  9. Implementation Details • Option of program being hosted public (server) / standalone application • Application server: JAVA servlets, JSP • Analysis Routine: C/ JAVA • Messaging System: HTTP • Communication Protocol: SOAP (xml format)

  10. Applications: • Pathway knowledge helpful in: • Deeper understanding of cell mechanisms under various conditions • Predict possible drug targets, • Provide end- user on most probable pathway set, from a publicly available database

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