Pathway-based gene expression data analysis
- Entropy based analysis of pathway involvement in diseases and differentiation
We develop entropy-based quantitative measures for biomolecular pathways' impact on cell differentiation
and disease development. This approach analyzes whole pathway activation dynamics based on its topology and time series gene expression data. Our approach
allows for stepping ahead from gene- to pathway-based level in understanding of molecular mechanisms of biological processes.
- Finding shared and specific pathway deregulation patterns in human diseases
The proposed project is aimed at investigating molecular pathomechanisms of complex human diseases via evaluation
of similarities and specificities of biomolecular pathway perturbations involved in their pathogenesis. For this purpose we use the GSS-PSF algorithm, and
phylogenetic and graph analysis techniques. The employed data analysis strategy will allow for identification of “candidate” genes which may become targets
for development of “universal” drugs for treatment of diseases characterized by common patterns of gene expression changes, as well as candidate genes that
may serve as targets for specific treatment of a particular disease.
Telomere length association with age related diseases and cancer
Telomere length is known to be associated with cellular ageing and replicative cell senescence. This association
is partially explained by telomere position effect (TPE), a phenomenon described by silencing of genes located near telomeres. It is proposed that this process
may be linked to several human diseases related to ageing and prolonged exposure to stress, as well as cancers. We are reconstructing gene regulatory networks
associated with TPE in normal and diseased states using NGS data. The results of this study will reveal genes associated with cellular senescence and tumor growth,
and their interconnections and links to disease development.
Development of software for KEGG pathway parsing and analysis
We develop software for parsing and visualization of KEGG pathway maps in MATLAB and in Cytoscape. In addition to parsing
and visualization, our software performs automatic corrections of inconsistencies inherent in KEGG xml formatted files (KGML files) and provides an easy interface
for manual editing and saving of KEGG pathway graphs. We are also developing features for tissue-specific tuning of the pathways based on available data on gene
activities in each tissue type, as well as pathway drill-down based on physical protein-protein interactions.