Seminars, events & talks

Wednesday, 19th November, 2014, 11:00-12:00

Recent Developments in RNA structure prediction and RNA design

Speaker: Ivan Dotu; Biology Department, Boston College.

Room Aula room (470.03 – 4th floor)

Monday, 10th November, 2014

b·debate "Big Data in Biomedicine. Challenges and opportunities". Sanz, Ferran. Comitè organitzador

Barcelona, 11-12/11/2014. organitzada juntament amb Bioinformatics Barcelona (BIB),  European Bioinformatics Institute (EMBL-EBI), Biocat i Fundació La Caixa.

Sunday, 9th November, 2014, 12:30

Bioinformatics in the Era of Big Data

Bio{medical}informatics is a field that has emerged through the analysis of data primarily generated by other researchers. As such it is a leading indicator for what biomedical research will involve in the era of big data, taken here to imply large diverse datasets at different biological   scales - from molecules to populations. Does the model by which we have accessed such data thus far scale to the future? I will argue that the answer is no and new approaches are needed which would change how we do our science.

Speaker: Philip E. Bourne, Associate Director for Data Science (ADDS) at the National Institutes of Health (USA)

Room Ramon y Cajal

Wednesday, 22th October, 2014, 11:00

Analysis and visualization of multidimensional cancer genomics data

Cancer is a complex disease caused by somatic alterations of the genome and epigenome in tumor cells. Increased investments and cheaper access to various technologies have built momentum for the generation of cancer genomics data. The availability of such large datasets offers many new possibilities to gain insight into cancer molecular properties. Within this scope we'll present two methods that exploit the broad availability of cancer genomic data: Oncodrive-ROLE, an approach to classify mutational cancer driver genes into activating and loss of function mode of actions and MutEx, a statistical measure to assess the trend of the somatic alterations in a set of genes to be mutually exclusive across tumor samples. Nevertheless, the unprecedented dimension of the available data raises new complications for its accessibility and exploration which we try to solve with new visualization solutions: i) Gitools interactive heatmaps with prepared large scale cancer genomics datasets ready to be explored, ii) jHeatmap, an interactive heatmap browser for the web capable of displaying multidimensional cancer genomics data and designed for its inclusion into web portals.

Speaker: Michael Schroeder - Biomedical Genomics group of GRIB

Room Sala Ramón y Cajal

Thursday, 16th October, 2014, 9:30 - 11:30

Bioinformatics: how it can support the FIC?

WHO-FIC Network. World Health Organization - Family of International Classification Annual meeting 11-17 October, 2014, Barcelona The WHO-FIC network is an International collaboration that aims to promote the appropriate selection of classifications in the range of settings in the health field across the world. The primary mandate of the Collaborating Centres for the WHO Family of International Classifications (WHO-FIC) is to support and promote the development and use of WHO classifications such as ICD (the International Classification of Diseases) and ICF (the International Classification of Functioning and Disability) in support of health and health services.

Speaker: Ferran Sanz

Room Centre de Convencions Internacional de Barcelona (CCIB) Plaça de Willy Brandt 11-14 - Barcelona

Tuesday, 7th October, 2014, 12:00

Clues to molecular mechanisms from the concerted action of genes

Speaker: Robert Castelo - Head of Functional Genomics group (GRIB)

Room Ramon y Cajal

Tuesday, 7th October, 2014, 8-14/10/2014

V Beyond The Genome: Cancer Genomics, Harvard Medical School, Boston ( USA). Lopez Bigas, Nuria. Organizing committee.

Thursday, 4th September, 2014, 11.00-12.00

Illuminating biology through navigating big data in drug discovery.

Methods like phenotypic screening, next generation sequencing and high content screening have become standard in drug discovery. All these methods yield a huge amount of data, which need to be processed and analyzed in order to be able to extract biologically relevant conclusions out of them. This novel, richer, more complicated data landscape means we need state-of-the-art in silico approaches to increase the probability of a lead compound to be disease relevant. This requires data analytics approaches informing on relevant assays, compound subset design to probe the biology, visualisation of complex biological data, and target/MOA hypotheses generation.

Speaker: Elisabet Gregori Puigjané; In silico Lead Discovery Group at the Novartis Institutes of Biomedical Research

Room Xipre (seminar 173.06-183.01), PRBB.

Monday, 30th June, 2014, 15.30-16.30

Systems Biology approaches to cellular metabolism: new possibilities for biopharma industry

Speaker: Francis Planes, Mechanical Enginering at School of Engineering at the University of Navarra.

Room Ramón y Cajal Room, PRBB.

Thursday, 26th June, 2014, 11.00-12.00

Modelling protein dynamics

Proteins are molecules involved in essentially all the complex biochemical reactions that take place in living organisms. In order to perform their functions they undergo conformational fluctuations on timescales ranging from nanoseconds to milliseconds and beyond. It is, therefore, important to develop methods capable of characterizing these motions. Nuclear magnetic resonance (NMR) spectroscopy is a powerful technique that enables the determination of the structures and dynamics of proteins at atomic resolution. Since NMR measurements produce values of observables resulting from time and ensemble averages, their interpretation is facilitated by considering ensembles of structures. The determination of an ensemble of conformations from experimental information about just average values is seem to be an ill-defined problem. In the seminar I will show that by using the Principle of Maximum Entropy (PME) it is possible to chose a special distribution (i.e., an ensemble of structures) among all those that are consistent with the experimentally-determined average values by imposing the average values themselves as thermodynamic constraints. This particular maximum entropy distribution provides an accurate representation of the unknown Boltzmann distribution of the system. The problem of determining structural ensembles can thus be solved unambiguously without making any additional assumption apart from the requirement that the experimental data should be consistent with it in the sense of the maximum entropy principle. To implement the maximum entropy principle in a computationally efficient manner, as we demonstrate in this paper in the case of NOE data, it is possible to use experimental measurements as replica-averaged structural restraints in molecular dynamics simulations.

Speaker: Andrea Cavalli, Institute for Research in Biomedicine, Department of Chemistry, University of Cambridge.

Room Xipre (seminar 173.06), PRBB.

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