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 : Modeling
Related people (11)
CV Senior Bioinformatician August 2015 – Present : Institut Pasteur, Paris PostDoc fellow 2011 – 2015 : Pascale Cossart’s laboratory, Unité des Interactions Bactéries-Cellules, Institut Pasteur, Paris Phd fellow 2007 – 2010 : Institut des Hautes Etudes Scientifiques, ann Ecole Normale Supérieure, Paris Magister of Science, Theoretical Physics 2003 – 2007 : Dynamical systems and statistics of complex matter, Université Paris 7 and Université Paris 6
BiophysicsMachine learningModelingProteomicsBiostatisticsDatabases and ontologiesHost-pathogen interactions
- Analysis of DNA methylation in the presence and absence of antibiotics in wt and mutant V. cholerae(Baharoglu ZEYNEP - Bacterial Genome Plasticity) - Closed
- Finding and Predicting CRISPR-Cas9 Efficiency(Jerome WONG NG - Synthetic Biology) - Closed
- Characterization of a Salmonella mutant carrying a single amino-acid substitution in the stress sigma factor RpoS(Françoise NOREL - Biochemistry of Macromolecular Interactions) - Closed
I received a Ph.D. in Biostatistics and Bioinformatics applied to Cancer Research in 2011 from the University Paris Sud XI, I was working at the Curie institute under the supervision of Emmanuel Barillot and François Radvanyi. My Ph.D. was about the unsupervised analysis of cancer transcriptome. During my postdoctoral time, I worked on the computational and statistical analysis of NGS data. My areas of interest and expertise include - functional genomics - human genetics - statistical analysis of high-dimensional data - normalization, batch-correction, meta-analysis of high-throughput data - unsupervised learning, independent component analysis - NGS data analysis (RNA-Seq, DNA-Seq, …) - analysis of the non-coding genome, transposable elements
- Left-right patterning of heart precursors(Tobias BØNNELYKKE - Heart Morphogenesis) - In Progress
- Collaboration between CETEA, C2RA and Hub for optimization of experimental designs (3R)(Myriam MATTEI - Center for Animal Resources and Research) - In Progress
- Mechanisms of HIV-1-infected cells susceptibility to Fc effector functions(Timothée BRUEL - Virus and Immunity) - In Progress
I joined the Bioinformatics and Biostatistics Hub at Institut Pasteur in 2016 where I am currently developing pipelines related to NGS for the Biomics Pôle. I have an interdisciplinary research experience: after a PhD in Astronomy (gravitational wave data analysis), I joined several research institute to work in the fields of plant modelling (INRIA, Montpellier, 2008-2011), System Biology — in particular logical modelling (EMBL-EBI Cambridge, U.K., 2011-2015), and drug discovery (Sanger Institute, Cambridge, U.K.), 2015). On a daily basis, I use data analysis and machine learning techniques within high-quality software to tackle scientific problems.
AlgorithmicsData managementData VisualizationGenome assemblyGenomicsMachine learningModelingScientific computingDatabases and ontologiesSofware development and engineeringData and text miningIllumina HiSeqGraph theory and analysisIllumina MiSeq
I have a joint MSc degree in Mathematical Modelling from three European universities: University of L’Aquila (Italy), University of Nice-Sophia Antipolis (France) and Autonomous University of Barcelona (Spain). I also hold a PhD degree in Applied Mathematics and Scientific Computing from University of Bordeaux, France. I have done my PhD and one year of post-doc at INRIA Bordeaux Sud-Ouest, and partially at IHU-Liryc. During this time I studied how electrical signals propagate through the cardiac tissue under certain diseased conditions. My model of interest was the bidomain model, which is a system of partial differential equations that takes into account physiological properties of the cardiac cells and the spatial organization of the cardiac tissue. I worked on the mathematical multiscale analysis and numerical simulations of the problem to understand how structural changes of the tissue affect the propagation of the signal on the heart level. I collaborated with biologists and engineers of the IHU-Liryc to apply my model on a rat heart using high-resolution MRI data. For this I also worked on image analysis and image processing. I’ve joined the Institute Pasteur in February 2018 as a member of the HUB in Bioinformatics and Biostatistics. Currently I am working on stochastic mathematical modeling and inference for systems biology, gene expression, RNA transcription, etc.
ModelingScientific computingApplication of mathematics in sciencesGraphics and Image Processing
BacteriaFungiInsect or arthropodEscherichia coliSaccharomyces cerevisiaeFly
- Modelization of the timing of abscission(Arnaud ECHARD - Membrane Traffic and Cell Division) - In Progress
- Estimation of the impact of differential apoptotic rate on local clone size(Romain LEVAYER - Cell death and epithelial homeostasis) - In Progress
- State and parameter inference for stochastic models of gene expression(Jakob RUESS - Other) - Closed
After a PhD in informatics on graph analysis (metabolic networks and sRNA-mRNA interaction graphs) at the LaBRI (Université de Bordeaux), I joined the DSIMB team (INTS) for a post-doc on structural modeling. Then, I performed a second post-doc at Metagenopolis – INRA Jouy-en-Josas, where I was initiated to the analysis of metagenomic data. I was recruited at the HUB in 2015, and since I pursue the development of methods dedicated to the treatment of metagenomic data by combining either the treatment of sequencing data, the statistics, the protein structural modeling and the graph analysis.
AlgorithmicsClusteringGenome assemblyGenomicsMetabolomicsModelingNon coding RNASequence analysisStructural bioinformaticsTargeted metagenomicsDatabaseGenome analysisBiostatisticsProgram developmentScientific computingDatabases and ontologiesExploratory data analysisData and text miningIllumina HiSeqComparative metagenomicsRead mappingIllumina MiSeqSequence homology analysisGene predictionMultidimensional data analysisSequencingShotgun metagenomics
- Evaluation of a novel mouse model for Primary Antibody Deficiency (PAD)(Lise HUNAULT - Antibodies in Therapy and Pathology) - In Progress
- Measles virus type 1 infection disturbs the mitochondrial network leading to type I interferon production through the RNA polymerase III/RIG-I pathway(Jean-Pierre VARTANIAN - Department of Virology) - Awaiting Publication
- Comparative analysis of choanoflagellate proteomic data(Thibaut BRUNET - Other) - Closed
Thomas is a biostatistician who holds an engineering degree in Agronomy (Agrocampus Ouest, Rennes, France). He also holds a Ph.D. in biostatistics from Université Pierre et Marie Curie for his work on the spread of nosocomial pathogens on contact networks. During his Ph.D at INSERM, he investigated how high-resolution dynamical contact data could support infection-tracing conducted using more traditional approaches in healthcare settings, e.g. routine swabbing and genetic characterization of strains detected in patients or healthcare workers. He developed a new statistical framework to test the correlation between dynamic close-proximity interaction networks and biological carriage data. While at INSERM, he also developed the R0 package for R that aimed at implementing several computation methods used in estimating reproduction parameters for emerging transmissible diseases. After working as a statistical modeller for a private company in the pharmaceutical industry, he joined the Hub in 2016 as a statistician and is now involved in the projects of the Malaria: parasites and hosts unit headed by Ivo Mueller.
ModelingBiostatisticsScientific computingApplication of mathematics in sciencesClinical researchEpidemiology and public health
- Deciphering the role of viral transmission on the establishment of persistent infections(Alessandro TORRI - Viruses and RNA Interference) - Pending
- Mosquito vector competence for Zika virus(Anna-Bella FAILLOUX - Arboviruses and Insect Vectors) - Closed
- Tests serologics anti-CNF1(Daniel GUERIN - Bacterial Toxins) - Closed
Since February 2017 Research engineer, Hub of Bioinformatics and Biostatistics of the C3BI, Institut Pasteur 2015-2017 Post doctoral position, team MISTIS, INRIA Grenoble Topic: Robust clustering and robust non linear regression in high dimension. Collaboration with Florence Forbes (INRIA). 2012-2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes Topic: Stability of variable selection in regression and classification issues for correlated data in high dimension. Supervisor: David Causeur (Agrocampus-Ouest, IRMAR). Education 2015 PhD thesis in Statistics, Applied Mathematics Department of Agrocampus-Ouest, IRMAR UMR 6625 CNRS, Rennes 2012 ISUP degree (Institut de Statistique de l’UPMC), Université Pierre et Marie Curie, Paris 2012 Master 2 of Statistics, Université Pierre et Marie Curie, Paris
ClusteringModelingStatistical inferenceTranscriptomicsBiostatisticsExploratory data analysisDimensional reductionStatistical experiment designMultidimensional data analysis
- Asymmetric heart morphogenesis(BERNHEIM SÉGOLÈNE - Heart Morphogenesis) - Closed
- Modulation of Flu transmission in Niger , according to climate variations over the past ten years(Ronan JAMBOU - Other) - Awaiting Publication
- Left-right patterning of heart precursors(Tobias BØNNELYKKE - Heart Morphogenesis) - In Progress
Dr. Natalia Pietrosemoli is an Engineer with a M. Sc. in Modeling and Simulation of Complex Realities from the International Center for Theoretical Physics, ICTP and the International School of Advanced Studies, SISSA (Triest, Italy). During her M. Sc. internships she mostly worked in modeling, optimization, combinatorics and information theory applied to medical imaging. In 2012 she got a Ph. D in Computational Biology from the School of Bioengineering of Rice University (Houston, TX, US), where she specialized in computational structural biology and functional genomics. Her doctoral thesis “Protein functional features extracted with from primary sequences : a focus on disordered regions”, contributed to a better understanding of the functional and evolutionary role of intrinsic disorder in protein plasticity, complexity and adaptation to stress conditions. As part of her Ph. D., Natalia was a visiting scholar in two labs in Madrid: the Structural Computational Biology Group at the Spanish National Cancer Research Centre (CNIO), where she mainly worked in sequence analysis and the functional-structural relationships of proteins, and the Computational Systems Biology Group at the Spanish National Centre for Biotechnology (CNB-CSIC ), where she studied the functional implications of intrinsically disordered proteins at the genomic level for several organisms, collaborating with different experimental and theoretical groups. In 2013, she joined the Swiss Institute of Bioinformatics as a postdoctoral fellow in the Bioinformactics Core Facility. Her main project consisted in the molecular classification of a rare type of lymphoma, which involved the integration of transcriptomic, clinical and mutational data for the identification of molecular markers for classification, diagnosis and prognosis. This work was performed in collaboration with the Pathology Institute at the University Hospital of Lausanne (CHUV). In November of 2015 Natalia joined the Hub Team @ Pasteur C3BI as a Senior Bioinformatician. Natalia is especially interested in the integrative analysis of different omics data, both at large-scale and for small datasets, and loves collaborating in interdisciplinary environments and having feedback from her fellow experimental colleagues. Currently, she’s coordinating several projects performing functional and pathway analysis at the genomic level. By grouping genes, proteins and other biological molecules into the pathways they are involved in, the complexity of the analyses is significantly reduced, while the explanatory power increases with respect to having a list of differentially expressed genes or proteins.
AlgorithmicsData managementGenomicsImage analysisMachine learningModelingProteomicsSequence analysisStructural bioinformaticsTranscriptomicsDatabaseGenome analysisBiostatisticsScientific computingDatabases and ontologiesApplication of mathematics in sciencesData and text miningGeneticsGraphics and Image ProcessingBiosensors and biomarkersClinical researchCell biology and developmental biologyInteractomicsBioimage analysis
- Exploring pathogenic mechanisms of chronic inflammatory disease: unresolved issues in IL-23/IL-17 biology(YAHIA HANANE - Immunoregulation) - In Progress
- Study of the role of cyclic dimeric guanosine mono-phosphate (c-di-GMP) in the regulation of virulence and biofilm formation in Leptospira interrogans(Gregoire DAVIGNON - Other) - In Progress
- Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms(Yves JACOB - Molecular Genetics of RNA Viruses) - In Progress
Hugo Varet is a biostatistician engineer from the Ensai (Ecole Nationale de la Statistique et de l’Analyse de l’Information) and has been recruited in 2013 by the Transcriptome & Epigenome Platform of the Biomics Pole. Late 2014 he obtained a permanent position at the Bioinformatics & Biostatistics Hub and has been detached to the platform to continue the statistical analyses of RNA-Seq data and develop R pipelines and Shiny applications that help in this task. One of them is named SARTools and is available on GitHub: https://github.com/PF2-pasteur-fr/SARTools. In December 2019 he left the Biomics Platform and joined the Bioinformatics & Biostatistics Hub as a core-member.
MetabolomicsModelingSequence analysisStatistical inferenceTranscriptomicsBiostatisticsScientific computingApplication of mathematics in sciencesExploratory data analysisHigh Throughput ScreeningClinical research
- Evaluation in cellulo of the impact of insecticide usage on arbovirus population(VALLET THOMAS - Viral Populations and Pathogenesis) - Closed
- Analysis of the molecular pathways induced by the activation of the Nod2 receptor by MDP in hypothalamic neurons(Ilana GABANYI - Perception and Memory) - Pending
- Identification of factors influencing the activity of bacteriophage within the gut of mammals(Devon CONTI - Other) - In Progress
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) - Closed
- Measles virus protein C interplay with cellular apoptotic pathways; applications for cancer treatment(Alice MEIGNIÉ - Viral Genomics and Vaccination) - Closed
- Diffusion des mutations de résistance du VIH : modèles et méthodes d’estimation(Olivier GASCUEL - Evolutionary Bioinformatics) - Closed
Related projects (5)
Lyme borreliosis is the most common tick-borne disease in the northern hemisphere. In Europe, it is transmitted by Ixodes ticks that carries bacteria belonging to the Borrelia burgdorferi sensu lato complex. Our study was focused on peri-urban forests of Île-de-France. These forests are frequented by many visitors and the risk of exposure to tick bites is high. One of them, the Sénart forest, is located 30 km south of Paris (in the Île-de-France region) and has a large number of visitors (3 million per year in the late 1990s). This forest has the characteristics of being partly invaded by chipmunks (Tamias sibiricus). The chipmunk has been introduced from Eurasia, particularly Siberia, China and Korea. The first individuals were released by their owners at the western end of the Sénart forest, in the 1970s. The northeastern part of the forest was colonized recently. Our current study aims to evaluate the evolution of the infection of Ixodes ricinus by Borrelia burgdorferi sl. by comparing the results obtained during 3 years and to determine the consequences of the proliferation of this non-native rodent species, Tamias sibiricus, on the risk of transmission of Lyme borreliosis. For this purpose, we analyzed the rate of infection and the density of infected ticks during 2008, 2009 and 2011 in several locations of the Sénart forest. These results were compared to those obtained for ticks collected in 2009 in two other peri-urban forests of Île-de-France (Rambouillet and Notre-Dame) that have not yet been colonized by these rodents. The density of nymphs, adults as well as the infected density of nymphs and adults were compared according to several factors: location of tick collection in the forest, presence or absence of chipmunks, type of vegetation, temperature and humidity.
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 molecular process, is regulated to achieve reliable embryonic patterning remains unclear. In particular, it is unknown how gene-specific regulation mechanisms affect kinetic rates of transcription, and whether there are common, global features that govern these rates across a genetic network. Quantitative measurements of nascent transcriptional activity in both living and fixed tissues are key in order to understand the underlying transcription kinetics and to make progress with these fundamental questions. The current project aims at constructing realistic minimalist models of transcription for different experimental and developmental contexts, using spatiotemporal gene expression activity data obtained from microscopic imaging of live biological tissues.
Cellular senescence is a complex stress response that durable (yet not irreversibly) arrests cell proliferation and is accompanied by widespread changes in chromatin structure, metabolism and gene expression including the production and secretion of a plethora of inflammatory factors. Cellular senescence plays beneficial roles during embryonic development, tissue regeneration, and tumor suppression. Paradoxically, it is also considered a major contributor to aging and age-related diseases, the latter mostly through its inflammatory phenotype, the so-called SASP (senescence-associated secretory phenotype). The proposed work aims at integrating time-resolved transcriptome, ChIP-seq, and ATAC-seq datasets into a comprehensive understanding of senescence-associated gene regulation.
Before the WHO considered it as a public health emergency of international concern in February 2016, Zika virus (ZIKV, Flavivirus, Flaviviridae) was a neglected mosquito-borne virus. First identified in Uganda in a sylvatic cycle, ZIKV has caused in few months millions cases, emerging in the five continents (Latin America, the Caribbean, Southeast Asia/Pacific Ocean, Africa/Indian Ocean, European countries (Portugal, Spain, France, Switzerland, the Netherlands)). We have initiated the most comprehensive study on vector competence with almost 50 mosquito populations belonging to five main species (Aedes aegypti, Aedes albopictus, Aedes japonicus, Culex pipiens, Culex quinquefasciatus) infected with 3 different ZIKV and examined at 3 days post-infection (7, 14, and 21). The objective of the project will be to run a meta analysis on vector competence and to assess to which extent each mosquito species contributes to ZIKV transmission according to the geographical location and the viral genotype. It will help to improve our understanding of the vector status and adapt surveillance, prevention, and control of Zika.