Tools
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.
Wednesday, 18th June, 2014, 11:00
The cancer genome interpreter identifying relevant cancer mutations in a patient
PRBB Computational Genomics Seminars.
Speaker: Abel González Pérez - Biomedical Genomics - GRIB
Room Sala 473.10
Wednesday, 21th May, 2014, 11:00
Exploring hibernation in mammals through transcriptomics: the case of the hibernating lemur
Hibernation is a complex physiological response some mammalian species employ to evade energetic demands. Hibernators conserve energy by essentially “shutting down” physiological processes; metabolic rate is severely depressed, body temperature plummets to ambient levels, and brain activity is greatly diminished (reviewed in Carey et al., 2003). In recent years the study of the molecular processes involved in mammalian hibernation has shifted from investigating a few carefully selected candidate genes to large-scale analysis of differential gene expression. The availability of high-throughput data provides an unprecedented opportunity to ask whether phylogenetically distant species show similar mechanisms of genetic control, and how these relate to particular genes and pathways involved in the hibernation phenotype.
In this talk, we are going to present our ongoing research in this field, comparing the genetic controls of hibernation in several mammalian species and presenting some results about the genetic regulation in the only primate known to naturally exhibit this behavior: the dwarf lemur (genus Cheirogaleus), endemic to Madagascar.
Carey, H. V, Andrews, M. T., & Martin, S. L. Mammalian hibernation: cellular and molecular responses to depressed metabolism and low temperature. Physiological reviews 2003; Vol: 83, 4 pages, doi:10.1152/physrev.00008.2003
Speaker: José Luis Villanueva - Evolutionary Genomics Group
Monday, 12th May, 2014, 11.00-12.00
Regression models for categorical responses: an application to the interaction of different chronic comorbidities in HIV positive patients
Multimorbidity is defined as the co-occurrence of two or more chronic medical conditions in one person. Multimorbidity is turning into a major medical issue for both individuals and health care providers. It is well-known that multimorbidity correlates with age and, furthermore, that HIV-infected patients experience an increased prevalence of noninfectious comorbidities, compared with the general population. It has also been hypothesized that such increased prevalence is the result of premature aging of HIV-infected patients.
Guaraldi et al. (2011) investigated the effect of HIV-infection on the prevalence of five noninfectious chronic medical conditions from an Italian dataset obtained from a cross-sectional retrospective case-control study. More specifically, Guaraldi et al. (2011) analysed the effect of HIV-infection by means of logistic regressions on univariate outcomes. However, the analysis of single responses is not sufficient becasue multimorbidity is characterised by complex interactions of co-existing diseases and to gain relevant insight on the role of HIV-infection on multimorbitiy it is necessary to use a multivariate approach aimed to investigate the effect of HIV on the interaction of different chronic conditions.
In regression models for categorical responses a linear model is typically related to the response variables via a transformation of probabilities called a link function. We present an approach that is based on the connection between two different links: the log-mean linear link and the Moebius link. In our framework, the interpretation of the effect of covariates on the interaction of responses is straightforward.
This is joint work with Monia Lupparelli, University of Bologna.
Speaker: Alberto Roverato, Professor of Statistics at the University of Bologna (Italy)
Room Xipre (seminar 173.06-183.01), PRBB.
Monday, 12th May, 2014, 10.00-11.00
Traceable regressions: general properties and special cases
Traceable regressions are those graphical Markov models that are best suited to capture generating processes in longitudinal studies, either without or with interventions. Sequences of responses may contain several component variables and these components may be continuous or discrete.
In this lecture, I discuss properties of corresponding distributions that are needed to read off the graph all implied independences, as well as the additional properties that permit similar conclusions about dependences. Some data analyses are shown and some results are discussed for star graphs, a very special type of graph.
Speaker: Nanny Wermuth, Chalmers University of Technology, Gothenburg and Johannes Gutenberg-University, Mainz
Room Xipre (seminar 173.06-183.01), PRBB.
