Human gut resistome

EVENT : C3BI Seminars


Main speaker : Amine Ghozlane, from HUB, C3BI Pasteur Date : 04-04-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Human gut resistome


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Bayesian matrix factorization for drug discovery and precision medicine

EVENT : C3BI Seminars


Main speaker : Yves Moreau, from Center for Computational Systems Biology, KU Leuven Date : 31-01-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Matrix factorization/completion methods provide an attractive framework to handle sparsely observed data, also called “scarce” data. A typical setting for scarce data are is clinical diagnosis in a real-world setting. Not all possible symptoms (phenotype/biomarker/etc.) will have been checked for every patient. Deciding which symptom to check based on the already available information is at the heart of the diagnostic process. If genetic information about the patient is also available, it can serve as side information (covariates) to predict symptoms (phenotypes) for this patient. While a classification/regression setting is appropriate for this problem, it will typically ignore the dependencies between different tasks (i.e., symptoms). We have recently focused on a problem sharing many similarities with the diagnostic task: the prediction of biological activity of chemical compounds against drug targets, where only 0.1% to 1% of all compound-target pairs are measured. Matrix factorization searches for latent representations of compounds and targets that allow an optimal reconstruction of the observed measurements. These methods can be further combined with linear regression models to create multitask prediction models. In our case, fingerprints of chemical compounds are used as “side information” to predict target activity. By contrast with classical Quantitative Structure-Activity Relationship (QSAR) models, matrix factorization with side information naturally accommodates the multitask character of compound-target activity prediction. This methodology can be further extended to a fully Bayesian setting to handle uncertainty optimally, and our reformulation allows scaling up this MCMC scheme to millions of compounds, thousands of targets, and tens of millions of measurements, as demonstrated on a large industrial data set from a pharmaceutical company. We also show applications of this methodology to the prioritization of candidate disease genes and to the modeling of longitudinal patient trajectories. We have implemented our method as an open source Python/C++ library, called Macau, which can be applied to many modeling tasks, well beyond our original pharmaceutical setting. https://github.com/jaak-s/macau/tree/master/python/macau.


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Deciphering gene expression programs at single-cell resolution

EVENT : JOINT Seminar C3BI –  DPT DE Biologie du développement et cellules souches


Main speaker : Stein Aerts, from Laboratory of Computational Biology. KU Leuven Center for Human Genetics. VIB Center for Brain and Disease Research. Date : 15-02-2019 at 11:00 am Location : Jules Bordet room – METCHNIKOFF (67) ,Institut Pasteur, Paris


Single-cell technologies are revolutionising biology and provide new opportunities to trace genomic regulatory programs underlying cell fate. In this talk I will present several computational strategies for the analysis of single-cell RNA-seq and single-cell ATAC-seq data that exploit the genomic regulatory code, to guide the identification of transcription factors and cell states. I will illustrate these methods on several model systems, including the Drosophila brain. Finally I will discuss how single-cell analyses can contribute to cross-species comparisons of regulatory programs.

Prof. Stein Aerts has a multidisciplinary background in both bio-engineering and computer science. During his PhD he was trained in bioinformatics, and during his Postdoc he worked on the genomics of gene regulation in Drosophila. Stein now heads the Laboratory of Computational Biology at the VIB Center for Brain & Disease Research and the KU Leuven Department of Human Genetics. His lab focuses on deciphering the genomic regulatory code, using a combination of single-cell and machine-learning approaches. His most recent scientific contributions include new bioinformatics methods for the analysis of single-cell gene regulatory networks, namely SCENIC and cisTopic. Aerts co-founded the Fly Cell Atlas consortium and generated a single-cell atlas of the ageing Drosophila brain (scope.aertslab.org). Stein holds an ERC Consolidator Grant and was awarded the 2017 Prize for Bioinformatics and Computational Science from the Biotech Fund and the 2016 Astrazeneca Foundation Award Bioinformatics.

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A Polymer Physics View on Universal and Sequence-Specific Aspects of Chromosome Folding

EVENT : C3BI Seminars


Main speaker : Ralf Everaers, from Laboratoire Physique ENS Lyon (UMR CNRS 5672) Date : 17-01-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Recent advances in genome-wide mapping and imaging techniques have strikingly improved the resolution at which nuclear genome folding can be analyzed and revealed numerous conserved features organizing the one-dimensional chromatin fiber into tridimensional nuclear domains. Understanding the underlying mechanisms and the link to gene regulation requires a crossdisciplinary approach that combines the new high-resolution techniques with computational modeling of chromatin and chromosomes. In the presentation I will discuss our current understanding of generic aspects of chromosome behavior during interphase. In collaboration with the Cavalli lab in Montpellier for the HiC experiments, we are using simulation techniques to explore their ability to explain the large scale chromosome folding in Drosophila nuclei during the course of development. We find that territory formation is fully described by the idea of topologically constrained relaxation of decondensing metaphase chromosomes. The characteristic signature of Rabl territories due to the memory of quasi-nematic chromosome alignment is visible during early stages of development, but disappears in late embryo nuclei. Compartimentalization of centromeric heterochromatin is well accounted for by co-polymer models with like-like attraction between hetero- and eu-chromatin. The additional distinction of a small number of epigenetic states allows to reasonably well predict the formation of (and interaction between) TADs.


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Integrated and spatial-temporal multiscale modeling of liver guide in vivo experiments in healthy & chronic disease states: a blue print for systems medicine?

EVENT : C3BI Seminars


Main speaker : Dirk Drasdo, from INRIA / IZBI Joint Research Group Date : 20-09-2018 at 02:00 pm Location : Salle Retrovirus – Bâtiment LWOFF ,Institut Pasteur, Paris


Background and Aims:  Hyperammonemia after drug-induced peri-central liver lobule damage, as from overdosing acetaminophen (paracetamol), and can lead to encephalopathy and dead of the patient. Guided by mathematical models, the consensus set of chemical reactions for detoxification of liver from ammonia has recently been shown to fail in explaining ammonia-detoxification after drug-induced peri-central damage (Schliess et. al., 2014). Our aim is to demonstrate how integrated and spatial-temporal models mimicking detoxification of the blood from ammonia in virtual tissue samples can assist in guiding identification of missing molecular mechanisms, or predicting the impact of micro-architectural alterations due to acute or chronic damage on ammonia detoxification. Our modeling methodology is very general.     Method:The consensus and alternative detoxification mechanisms have been implemented within mathematical integrated and spatial-temporal multi-scale models to test various hypotheses on potentially missing mechanisms in ammonia detoxification during liver regeneration after drug-induced pericentral damage in silicoin a virtual liver lobule (Drasdo et. al., J. Hepat. 2014). The multi-scale model simulates blood flow and molecular transport in the spatial lobule micro-architecture and displays each individual hepatocyte in space and time. Detoxification reactions are executed in each virtual hepatocyte. This makes in silicotesting of hypothesized mechanisms feasible from the molecular up to the tissue scale. The results are directly compared to experiments in mouse. Finally, fibrotic streets have been added to the model to predict the possible impact of architectural distortions and micro-shunts.     Results:We demonstrate how multiscale and multilevel models guided experiments towards identification of a previously unrecognized ammonia detoxification mechanism, that has the potential of improving treatment in hyperammonemia (Ghallab et. al., J. Hepat. 2016). The same model predicts for CCl4-induced fibrosis a reduced detoxification capacity for ammonia. Finally we outline how the whole body scale can be included to arrive at a model spanning molecular up to whole body scale permitting to study the relation of molecular changes and micro-architecture on whole body blood circulation, and briefly summarize results of integration of APAP toxic pathway as HGF signaling.    

Conclusion:Refined multi-scale models increasingly permit realistic prediction of liver function as well as of toxic injury in acute and chronic damage states. Those models can integrate data from various sources, in vitro, different animal models or human data. The direct representation of liver micro-architecture in those models will open up the future perspective to feed these models with patient-specific data, hence generating a virtual twin of a patients’ liver to guide personalized diagnosis and therapy planning.


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Signatures of ecological processes in microbial community time series

EVENT : C3BI Seminars


Main speaker : Karoline Faust, from KU Leuven Date : 04-10-2018 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Nowadays, a number of densely sampled microbial community time series is available, where the abundance of community members is tracked over several months through sequencing. These data allow exploring community dynamics by investigating signatures of underlying ecological processes that are present in the community time series. In this seminar, I will present our work on the exploitation of time series properties to distinguish between different ecological processes behind the observed dynamics

  http://psbweb05.psb.ugent.be/conet/karoline/

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Computational microbial genomics

EVENT : C3BI Seminars


Main speaker : Zamin Iqbal, from Royal Society/Wellcome Trust Sir Henry Dale Fellow, EMBL-EBI Date : 07-03-2019 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


TBA


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Unit Seminar – Lucas Husquin & Jakob Ruess

 

EVENT : C3BI Unit Seminars


Main speaker : Lucas Husquin, from Human evolutionary genetics unit Date : 15-02-2018 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Lucas Husquin (Human evolutionary genetics unit) : “Dissecting the impact of population variation in DNA methylation on transcriptional responses to immune activation”

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Jakob Ruess (InBio : Experimental and computational methods for modeling cellular processes) : “Virtual reality for bacteria”


Unit Seminar – Frédéric Lemoine & Lyam Baudry

 

EVENT : C3BI Unit Seminars


Main speaker : Frederic Lemoine from Bioinformatique evolutive Unit Date : 18-01-2018 at 02:00 pm Location : Auditorium Francois Jacob – BIME (26) ,Institut Pasteur, Paris


Frederic Lemoine (Bioinformatique Evolutive) : “Renewing Felsenstein’s Phylogenetic Bootstrap in the Era of Big Data”

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Lyam Baudry (Spatial Regulation of Genomes) : “Metagenome binning using chromosome conformation capture (3C) data”


Seminars – Statistical design and analysis of reproducible quantitative mass spectrometry-based experiments

EVENT : C3BI Seminars

Statistical design and analysis of reproducible quantitative mass spectrometry-based experiments


Main speaker : Olga Vitek, from Northeastern University, Boston, MA, USA
Date : 07/12/2017 at 02:00 pm
Location : Salle Retrovirus-LWOFF, Institut Pasteur, Paris


Statistical methodology is key for quantitative proteomics, as it helps reduce bias and inefficiencies,  distinguish the systematic variation from random artifacts, and maximize the reproducibility of the results. This talk will overview the statistical methodology implemented in MSstats, an open-source R package for statistical relative quantification of proteins and peptides. MSstats supports experiments with complex designs, such as comparisons of multiple groups or time course comparisons. It handles quantitative shotgun DDA (data-dependent acquisition) experiments, targeted SRM (selected reaction monitoring), and SWATH/DIA (data independent acquisition) experiments. It can be used in conjunction with label-free experimental workflows, or with workflows that utilize stable isotope reference proteins or peptides. MSstats contains functionalities for data processing, model-based statistical analysis (including testing proteins and peptides for differential abundance, or estimating protein abundance on a relative scale), and model-based calculation of a sample size for a future experiment. It also contains functionalities for systems suitability and statistical process control, and for quantification of figures of merit (such as limit of detection) of mass spectrometric assays. MSstats is available stand-alone or via graphical user interface as an external tool in the software framework Skyline. It can be interfaced with numerous spectral processing tools, such as MaxQuant.


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