Hub members Have many expertise, covering most of the fields in bioinformatics and biostatistics. You'll find below a non-exhaustive list of these expertise
Searched keyword : Systems Biology
Related people (2)
After graduating from Paris VI University with a PhD in Genetics on the “Role of histone protein post-translational modifications in splicing regulation” that I performed in the Epigenetic Regulation unit at the Institut Pasteur, I carried out two post-doctoral experiences. I first worked for three years as a postdoctoral associate of the Whitehead Institute for Biomedical Research/MIT in Cambridge (USA). My main project consisted in the integration of genomic and epigenomic data in order to predict the transcription factors that are potentially at the core of the regulation of the cell-type specific gene expression programs. I then joined the Institut Curie where I deepened my experience in multi-omics data analyses and integration to identify non-coding RNAs involved in cancer progression. I have recently joined the HUB-C3BI of the Institut Pasteur where I am performing high-throughput data integration to better understand biological complexity and contribute to precision medicine development.
ATAC-seqChIP-seqEpigenomicsNon coding RNAPathway AnalysisRNA-seqSingle CellSystems BiologyTool DevelopmentTranscriptomicsData integrationGraph theory and analysisCell biology and developmental biology
A computer scientist by training, I am applying this knowledge to solve biological problems and am particularly interested in modelling of biological systems, knowledge inference, ontologies and data visualisation.
AlgorithmicsData VisualizationMetabolomicsModelingPathway AnalysisPhylogeneticsSystems BiologyTool DevelopmentDatabaseProgram developmentScientific computingDatabases and ontologiesApplication of mathematics in sciencesSofware development and engineeringData and text miningEvolutionData integrationGraph theory and analysisWorkflow and pipeline developmentDiscrete and numerical optimization
VirusHuman Immunodeficiency virus (HIV)
- Modeling mitochondrial metabolism dormant Cryptococcus neoformans(Benjamin HOMMEL - Molecular Mycology) - In Progress
- Measles virus protein C interplay with cellular apoptotic pathways; applications for cancer treatment(Alice MEIGNIÉ - Viral Genomics and Vaccination) - In Progress
- Diffusion des mutations de résistance du VIH : modèles et méthodes d’estimation(Olivier GASCUEL - Evolutionary Bioinformatics) - In Progress
Related projects (8)
A long-term mission for an assigned CIH-embedded bioinformatician to provide bioinformatic support to the CIH community
The Center for Human Immunology (CIH) supports researchers involved in translational research projects by providing access to 16 different cutting edge technologies. Currently, the CIH hosts over 60 scientific projects coming from 8 departments of the Institut Pastuer and 5 external teams. In order to respond to the growing needs of these projects in the area of single cell analysis, the CIH has introduced a significant number of single-cell/single-molecule technologies over the past 2-3 years. These new technologies, such as the Personal Genome Machine (PGM) and Ion Proton sequencers, iSCAN microarray scanner, Nanostring technology for transcriptomics profiling and real-time PCR machine BioMark, give rise to large datasets with high dimensionality. Such trend, in terms of data complexity, is also true for flow cytometry technologies (currently reaching over 20 parameters per cell). The exploration of this data is generally beyond the scope of scientists involved in translational research projects. In order to maximize the research outcomes obtained from the analysis of these rich datasets, and to ensure that the full potential of our technologies can be served to the users of the CIH, we would require a proximity bioinformatics support. A CIH-embedded bioinformatician would: 1) design and implement standard analysis pipelines for each of the data-rich technologies of the CIH; 2) provide regular ‘bioinformatics clinics’ to allow scientists the possibility to customize standard pipelines to their specific needs; 3) run trainings on the ‘R software’ platform and other data analysis tools (such as Qlucore) of interest for the CIH users. The objective would be to empower the users to run exploratory analysis by themselves, and to teach good practices in terms of data management and data analysis.
We would like to uncover associations between transcriptomic features and dengue infection outcome. In order to do so, we want to take advantage not only of our data but also of all publicly available data. A main challenge is to translate the measurements across different transcriptomic technologies into a summary expression level per gene. For this C3BI project we would like to concentrate on the problem of mapping probe IDs into a common identifier for all experiments.
In the context of the Swiss consortium InfectX (www.infectx.ch), Javier PIZARRO-CERDA previously performed siRNA, microRNA, drug screens and proteomic analyses to investigate signaling pathways modulating invasion of host cells by the bacterial pathogen Listeria monocytogenes. In a first consortium study, based on results from drug and siRNA screens targeting the human kinome, we identified major kinases which up- or down-regulate cell invasion by L. monocytogenes and by 7 additional bacterial and viral pathogens (Rämö et al. 2014). Subsequently, a siRNA genome-wide screen allowed us to revisit and redefine the role of cytoskeletal complexes required for L. monocytogenes cellular invasion and actin-based motility (Kühbacher et al. 2015). Applying a proteomic ‘surfaceome’ analysis, we also revealed that late endosomal compartments are recruited to L. monocytogenes infection foci to promote invasion (Kühbacher et al. Submitted). More recently, we have started the analysis of a microRNA screen which highlights novel gene clusters associated to regulation of phosphoinositide metabolism during L. monocytogenes cell entry (Kühbacher et al. Unpublished Results). These different projects have generated vast amounts of data that have been until now only independently analyzed. However, this information can now be exploited from a systems biology perspective to identify hidden connections between relevant signaling cascades and gene networks which may highlight novel cellular functions exploited by pathogens in the context of infection. The team of Benno SCHWIKOWSKI will perform two types of analysis on the data generated by Javier PIZARRO-CERDA. In both cases, p-values will be aggregated across gene sets using suitable statistical approaches. We will then
- Pathway-based analysis. This type of analysis considers genes in sets that have been recognized to operate together to perform certain biological functions (e.g.,
Measles virus protein C interplay with cellular apoptotic pathways; applications for cancer treatment
Measles virus protein C interplay with cellular apoptotic pathways; applications for cancer treatment.
Cryptococcus neoformans is a ubiquitous yeast present in the environment that is able to interact closely with numerous organisms including amoeba, paramecium or nematodes. The interaction with these organisms shaped its virulence with acquisition of infectious properties as a consequence especially in mammals . The ability to survive nutrient starvation, oxidative stress, desiccation, both in the environment and during infection, indicates a high level of physiological and metabolic plasticity of the yeast. In humans, after primary infection during childhood, the yeast is able to survive within the host for years before reactivation upon immunosuppression, leading to a life threatening disseminated fungal infection. This phenomenon, called dormancy / quiescence is one of the main biological features of this fungus in relation with disease's pathogenesis. It is well known in bacteria (tuberculosis), parasites (Plasmodium, Toxoplasma). In C. neoformans, dormancy has only been demonstrated epidemiologically in our laboratory but not experimentally so far. We developed an assay where yeasts cells exhibiting characteristics of potentially dormant cells were generated. Indeed, dormant cells are characterized by a low metabolic activity sometimes undetectable under normal laboratory conditions, altered growth capacity, and the ability to resuscitate upon adequate stimulus. Dormant cells are known to have increased mitochondrial masse and activity justifying a screening strategy of a collection of KO mutants for mitochondrial proteins. In parallel the whole proteome, transcriptome and secretome will be obtain with the ambition to correlate these parameters. Our current project aims at exploring the metabolism of the dormant yeast to have a comprehensive picture of the pathways that are required for the maintenance of dormancy and fo exit from dormancy.
Common and phylogenetically widespread coding for peptides by bacterial small RNAs – Follow up of a project regarding its journal review
Following a collaboration started a few years ago between a postdoc of the System Biology team (Robin Friedman) and Olivia Doppelt-Azeroual, a publication is in review in the journal Genome Biology. One of the reviewers made comments regarding the database and web interface implemented by Olivia at the time and after a brainstorm on the review, the first author (Robin) needs to make a few modifications on the database. This modification requires Olivia's intervention to update the database and adapt the web application accordingly, in order to display the right information: adding a column in the table with the concerned sRNA names.
Quantitatively understanding the stochastic dynamics of gene expression requires measurements at the level of single cells. A common approach to follow the expression of genes in single cells and in real time is to make use of fluorescent reporter proteins and to record the cells' fluorescence by microscopy. However, this provides only an indirect readout of the biological processes that are of interest such as the regulation mechanisms at the promoter. A possible way to uncover the unobservable biological processes is to infer the hidden dynamics from the available data through the use of mechanistic models of gene expression. The goal of this project is to develop methods for state estimation and parameter inference for such models and to test these methods on real data.
In early development, regulation of transcription results in precisely positioned and highly reproducible expression patterns that specify cellular identities. How transcription, a fundamentally noisy