Integrative analysis of diverse large-scale data sets for Hepatitis C disease
Benno Schwikowski
Systems Biology Group, Pasteur Institute, Paris
The extraction of biological knowledge from genome-scale data sets
requires its analysis in the context of additional biological
information. The importance of integrating experimental data sets
with molecular interaction networks has been recognized and applied
to the study of model organisms, but its systematic application to
the study of human disease has lagged behind due to the lack of tools
for performing such integration.
We have developed analysis approaches and software tools for the
integration of diverse experimental data types in molecular networks.
We applied these techniques to extract, from genomic expression data
from Hepatitis C virus-infected liver tissue, potentially useful
hypotheses related to the onset of this disease. Our integration of
the expression data with large-scale molecular interaction networks
and subsequent analyses identified molecular pathways that appear to
be induced or repressed in the response to Hepatitis C viral infection.
The methods and tools we have developed allow for the efficient
dynamic integration and analysis of diverse data in a major human
disease system. This integrated data set in turn enabled simple
analyses to yield hypotheses related to the response to Hepatitis C
viral infection.