[Todos] Nuevo Seminario del Instituto de Cálculo: Matemática y Biología

[gduran en dm.uba.ar]

Seminario de Matemática Aplicada e Industrial del Instituto de Cálculo

Jueves 21 de Junio de 2012 - 12 hs.

Lugar: Instituto de Cálculo, Segundo Piso, Pabellón II, Ciudad Universitaria

Matemática y Biología: “From Protein Quantitation to Biomarkers Discovery:
Statistical Challenges and Solutions”

Expositora: Gabriela Cohen Freue (Dep. Estadística, Universidad de British
Columbia, Canadá)

Resumen: To date, there is an unmet clinical need to identify molecular
indicators (e.g., proteins) of
various diseases, including cancer, heart failure, and chronic obstructive
pulmonary disease, among
others. These indicators (i.e., biomarkers) can yield to the development
of minimally-invasive and
time-effective diagnostic tools improving patients' care and decreasing
costs in healthcare systems.
Recent advances in genomics, the study of gene expression patterns, and
proteomics, the study
of phenotypic protein abundances, open new venues for these biomedical
investigations focused
on the identication of these molecular biomarkers. To date, the number
and the quality of the
technical resources available for these biomarker studies and the rapid
expansion of genomic and
proteomic datasets are well recognized. However, the development of
tailored statistical methods
to address the challenges that have arisen in the eld has lagged behind,
dramatically reducing the
pace, quality and precision of biomarker studies.
This talk will give a brief overview of the main proteomic technologies
currently used in
biomarker studies, and outline the key steps required to analyze the rich
proteomic data generated by these technologies. Using a case study of
cardiac transplantation we describe some of the
challenges that the statistical proteomics community is facing today. In
particular, I will be focused
on the problem of measurement errors in mass spectrometry proteomic
quantitation, which may
afect the identication of protein biomarkers in a discovery study. As
protein levels are regulated
in part by gene expression, related genomic data can be integrated to
address this problem through
the implementation of instrumental variables estimators. The proposed
methodology exploits, in
an intuitive way, the plausible mechanisms from existing biological
knowledge that relate genes,
proteins, and diseases and takes advantage of this knowledge to increase
the signal strength of
sometimes weak, but real and biologically relevant -omics signatures.