[Todos] Recordatorio Seminario Jueves 3 de Diciembre, a las 14hs

[Adrián Turjanski]

Jueves 3 de Diciembre, a las 14hs
Aula Busch, primer piso, depto quimica inorganica.

Challenges in the integration of genomic and phenotypic information on
models of cell signaling pathways
Laura Inés Furlong, Integrative Biomedical Informatics laboratory,
Research Unit on Biomedical Informatics (GRIB)
IMIM/UPF, Barcelona, Spain (lfurlong en imim.es,
http://ibi.imim.es/LFurlong.html)

Abstract
During the talk the lines of research currently undergoing at the
Integrative Biomedical Informatics (IBI) lab will be
presented briefly, followed by a more detailed description of our approach
for the integration of genomic and phenotypic
information on biological networks. The research goal of the IBI lab is to
understand the molecular mechanism underlying
diseases and drug responses, and for that we use several bioinformatic
approaches, ranging from data and text mining to
topological and dynamical analysis of biological networks. Our work
involves the study of the cellular and biological
processes (metabolic pathways, signal transduction pathways and gene
regulatory networks) associated to disease
phenotypes, which are collected from public databases and the biomedical
literature. However, information found in public
databases often represent a general description of such processes, with
few details on the aspects that are specific for each
disease state. Thus, for obtaining the biological processes that are
actually involved in a certain disease, it is necessary to
collect and integrate other pieces of information that provide a better
definition of the biological process affected in the
disease state. One example is the information on mutations and
polymorphisms associated to different diseases, and our
strategy for the integration of this information with biological networks
will be presented, which includes the evaluation of
the effect of the mutation or polymorphism on the dynamics of the
biological process. Finally, results on the analysis of a
global view of gene-disease association networks will be presented.