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Seminars, events & talks

Friday, 21th April, 2017, 13:00


Big data in chemical safety assessment, challenges and opportunities

BIGCHEM BCN 2017: school about Computational Chemistry and Pharmacology.  Traditional methods for chemical safety assessment based in animal testing are being replaced by alternatives approaches, more acceptable from an ethical point of view. This situation gives the opportunity to exploit the vast amount of data already obtained from in vivo studies and other sources, even if the application of this concept in practice is being more difficult than expected.

Speaker: Manuel Pastor - Head of of the PharmacoInformatics group of GRIB

Room Charles Darwin Room - PRBB

Thursday, 20th April, 2017, 13:00

Systems Pharmacology

Large-Scale Predictive Drug Safety

BIGCHEM BCN 2017: school about Computational Chemistry and Pharmacology. The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety that pave the way towards gaining a better understanding of the mechanisms leading to adverse outcomes.

Speaker: Jordi Mestres - Head of the Systems Pharmacology group of GRIB

Room Charles Darwin Room - PRBB

Friday, 10th March, 2017, 11:00-12:00

Computational Biophysics

Structural prediction of protein-protein interactions for the upcoming challenges in biomedicine

Speaker: Juan Fernández Recio, Research Director, BSC.

Room Ramón y Cajal Room, PRBB Building

Thursday, 9th March, 2017, 12:00

Computational RNA Biology

Characterization of RNA processing alterations in small cell lung cancer

Small cell lung cancer (SCLC) accounts for 15% of all lung cancers. Previous studies have shown high frequency of mutations in TP53 and RB1, and amplification of MYC. However, no targeted therapies have been approved for use in treatment of SCLC, contrary to other lung cancer types like adenocarcinoma. Accordingly, chemotherapy remains the only treatment, which is initially effective but is inexorably followed by rapid relapse in the majority of the patients. Understanding the molecular mechanisms underneath this disease is thus necessary for improving treatment. We have analyzed RNA-seq from 73 RNA-seq SCLC patient samples from and characterized the transcriptomic changes between tumor and normal tissues. We have validated these changes on other 2 cohorts of 31 and 19 RNA-seq SCLC patient samples. In order to identify those changes specific of SCLC, and to account for the fact that SCLC tumors have different cell type of origin than other lung tumors, we performed comparisons against more than 1000 non-small cell lung samples from The Cancer Genome Atlas and against neuroendocrine lung carcinoid tumors. Additionally, using 71 WGS SCLC samples, we looked for somatic mutations disrupting intronic and exonic splicing regulatory motifs that could be responsible for these changes in the transcriptome. This is the largest analysis performed to date of RNA processing alterations and associated mutations in SCLC, which could lead to the uncovering of novel targets of therapy.

Speaker: Juan Luís Trincado

Room Aula room 473.10 (PRBB, 4th floor)

Thursday, 2nd March, 2017, 12:00

Functional Genomics

Peeking at incomplete penetrance with linkage analysis

Large-scale genetic profiling and clinical sequencing are revealing an increasing number of carriers of disease-causing mutations who do not develop the disease phenotype. This characteristic is clinically reported as a genetic disorder of reduced or incomplete penetrance. Several mechanisms have been proposed to explain incomplete penetrance, such as the molecular context of mutations, patient characteristics, such as age or sex, as well as specific environmental conditions that delay or trigger the disease onset. The phenomenon of incomplete penetrance constitutes a major challenge in the field of genetic diagnosis and counseling because phenotypes no longer unambiguously exhibit underlying genotypes. Nevertheless, its existence also provides new opportunities to learn how genotypes shape phenotypes. In this talk I will discuss our efforts using linkage analysis, to find a genetic modifier that explains the incomplete penetrance of a specific genetic disorder.

Speaker: Pau Puigdevall, Functional Genomics, GRIB, UPF

Room Aula room 473.10 (4th floor)

Thursday, 23th February, 2017, 12:00

Integrative Biomedical Informatics

Identifying temporal patterns in patient disease trajectories: a population-based study

The widespread use of electronic health record (EHR) datasets has facilitated the massive collection of patient health information, thereby enabling researchers to conduct large-scale studies of comorbidities. The term comorbidity can be defined as the co-occurrence of two or more diseases within the same individual. The factor of time has, typically, not been taken into account in most of the relevant works. However, by incorporating the time dimension into a comorbidity study more complex disease patterns and their temporal characteristics can be revealed. In this work, a large-scale temporal comorbidity study is performed on a local (Catalonian) health database. The disease-history vectors of individual patients are compared between each other in order to extract common disease trajectories (i.e. shared by at least 2 patients). By using statistical-significance tests on the common disease trajectories of length=2, significant pairwise disease associations are identified and their temporal directionality is assessed. Subsequently, a novel unsupervised clustering algorithm, based on the Dynamic Time Warping (DTW) technique, is applied on all extracted common disease trajectories (length>=2), in order to group them according to the temporal patterns that they share. It will be shown that DTW can successfully cluster the disease-trajectory signals under investigation, which consist of various time scales and durations, although they do not exhibit any obvious temporal alignment. In this manner, important key clusters can be identified with trajectories that share the same time-dependent characteristics. A time-dependent comorbidity analysis is expected to facilitate the early diagnosis of a disease and prevent any adverse outcomes, by permitting the prediction of the disease progression along time.

Speaker: Alexia Giannoula

Room Aula room 473.10 (4th floor)

Thursday, 2nd February, 2017, 12:00

Evolutionary Genomics

"Comparative Transcriptomics and RiboSeq: Looking at De Novo Gene Emergence in Saccharomycotina"

In de novo gene emergence, a segment of non-coding DNA undergoes a series of changes which enables transcription of the segment, potentially leading to a new protein with a novel function. What makes de novo genes different from other genes? Due to their unique origins, young de novo genes have no homology with other genes and may not initially be under the same selective constraints. While dozens of de novo genes have been observed in many species, the mechanisms driving their appearance are not yet well understood. To study this phenomena, we have performed deep RNA-seq and ribosome profiling (RP) on 11 species of yeast from the phylum of Ascomycota in both rich media and oxidative stress conditions. These data have been used to classify the conservation of genes at different depths in the yeast phylogeny. Hundreds of genes in each species were novel (non-annotated), and many were identified as putative de novo genes; these can then be tested for signals of translation using our RP data. We show that putative de novo genes have different properties when compared to phylogenetically conserved genes. Understanding the mechanisms behind de novo gene emergence in a 'simple' eukaryote like S. cerevisiae may help to explain some of the unique adaptations seen in more complex organisms.

Speaker: Will Blevins (Evolutionary Genomics group of GRIB)

Room Aula room 473.10 (4th floor PRBB)

Friday, 20th January, 2017, 13.00 - 14.00

Computational Biophysics

Protein dynamics and molecular design: computational approaches with an eye to chemical biology

Speaker: Giorgio Colombo, Instituto di Chimica del Riconoscimento Molecolare, CNR, Italia​,

Room ​ Xipre Room (Seminar ​173.06-183.01), PRBB Building

Thursday, 19th January, 2017, 12:00

Evolutionary Genomics

"Evolution by innovation: Is the de novo emergence of coding genes a myth or a mystery - or both?"

In this lecture I will review the basics behind what makes proteins the most basic elements on which selection acts, how they mediate evolvability of organisms and why it seems so unlikely that a protein emergence de novo, i.e. by creation of a new ORF from previously untranscribed DNA. Since such emergence has, however, been observed we -- and many other groups around the world -- are desperately trying to resolve this mysterious puzzle which puts two fundamental schools of thought -- biophysics and genetics -- at odds.

Speaker: Erich Bornberg-Bauer Molecular Evolution and Bioinformatics. Institute for Evolution and Biodiversity. Universität Münster. Germany.

Room Aula room 473.10 (4th floor)

Thursday, 15th December, 2016, 12:00

Computational RNA Biology

"Intratumoral evolution of breast cancer in response to therapy"

Population heterogeneity within tumors is essential to the development of drug resistance. However, precise quantification of cellularity levels of subpopulations, and in particular how they evolve in response to treatment, has been challenging. Here we describe the high precision characterization of subclonal evolution within triple-negative breast cancer patient-derived xenografts (PDXs) generated from three patients in response to multiple chemotherapies, covering >100 total samples and allowing for extensive intratumoral comparisons. Computational mutation and copy number analysis from post-treatment sequencing indicated sample-specific differences in tumor populations both in response to treatment and due to genetic drift. I will describe the evolutionary behaviors we have observed, which include selective sweeps, spatial diffusion, and symbiosis.

Speaker: Jeffrey Chuang, Ph.D, The Jackson Laboratory for Genomic Medicine; University of Connecticut Health Center Dept. of Genetics and Genome Sciences; Host: Eduardo Eyras

Room Aula room 473.10 (4th floor)

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