In the Lab, we question the computational basis of environment exploration and decision making from microscopic to macroscopic dynamics. We approach these topics through collaborative experiments and through theoretical approaches combining Physical based modelling, Bayesian Inference and Statistical Theory.
The huge amount of molecular data available nowadays can help addressing new and essential questions in evolution. However, reconstructing evolution requires models, algorithms, and statistical and computational methods of ever increasing complexity. Out unit aims at developing new methodologies and algorithms that are able to tackle efficiently the ever increasing amount of sequencing data, in the field of evolution and molecular phylogeny. The preferred applications relate to the evolution of viruses and other pathogens, in correlating their evolutionary history and available epidemiological data.
InBio is an interdisciplinary research group, combining wet and dry biology in the same lab. We employ systems and synthetic biology approaches with control and active learning methods, together with stochastic and statistical modeling frameworks. Our main long-term goal is to develop a comprehensive methodological framework supporting the development of a quantitative understanding of cellular processes. Given a process of interest and current knowledge on the system, the problem is to decide iteratively which strain to construct and which experiment to run to characterize the process in an optimal manner. More generally, we are interested in understanding, controlling and optimizing cellular processes from the single cell to the cell population levels. Past and current applications include (i) real-time control of gene expression using optogenetic and chemical stimulations in various systems (e.g. gene expression in yeast and bacteria, toggle switch in bacteria), (ii) understanding the origins of gene expression variability in response to Hog pathway induction in yeast, (iii) characterizing the dynamics of phenotypic heterogeneity in connection with reversible resistance to repeated anticancer treatments in Hela cells, and (iv) characterizing collective antibiotic resistance in ESBL-producing bacteria. On the methodological side, we employ single cell models (mixed-effects models, stochastic processes) to represent the biological processes and develop methods for model reduction, sensitivity analysis, inference of model parameters, experimental design, and control, based on techniques such as global optimization, stochastic simulation, and moment closure, among others. In addition to software that support these methodological developments, we also develop software for videomicroscopy image analysis and for microscopy automation. InBio is an Inria – Pasteur Institute joint research group. It is hosted at Institut Pasteur and affiliated to the Lifeware team at Inria Saclay – Ile-de-France. Close collaborators include, in addition to Lifeware members, Eugenio Cinquemani (Inria Grenoble), Dirk Drasdo (Inria Paris), Calin Guet (IST Austria), Pascal Hersen (MSC lab, CNRS and Paris Diderot), Gasper Tkacik (IST Austria), Lingchong You (Duke University), and Christoph Zechner (Max-Planck Institute for Molecular Cell Biology & Genetics).
“Nature is the best doctor: she cures three out of four illnesses, and she never speaks ill on her colleagues.” Louis Pasteur Our research is focused on understanding how natural selection, human demography and lifestyle have shaped the patterns of diversity of the human genome, to understand how this may impact phenotype variation and disease. Our current projects aim to to increase our understanding of (i) the genetic architecture of human populations, migrations patterns and admixture events; (ii) the occurrence of positive selection in the human genome and the relationship between population demography and the burden of deleterious alleles; (iii) the genetic and epigenetic determinants of immunity-related traits, with an emphasis on molecular phenotypes such as gene and miRNA expression; and (iv) the relationship between genetic diversity, epigenetic patterns (in particular DNA methylation) and changes in lifestyle and habitat of human populations. To this end, our laboratory combines population genetics and cellular genomic approaches, with computational modelling and development of new statistical frameworks, often working closely to theoretical population geneticists, immunologists, epidemiological geneticists as well as anthropologists.
Our group gathers psychiatrists, neuroscientists and geneticists to understand the causes of autism spectrum disorders (ASD). We previously identified one synaptic pathway associated with ASD – the NLGN-NRXN-SHANK pathway. This pathway is known for playing a role in synapse formation and in the balance of excitation and inhibition within the brain. In parallel, we identified the first mutations within the melatonin pathway, which could contribute to the sleep problems observed in individuals with ASD. Our results highlight the genetic heterogeneity of ASD, but also point at common pathways that could constitute relevant targets for new treatments. We are currently performing a thorough genomic and clinical profiling of a large number of individuals (>500 families with ASD) using high-throughput genotyping/sequencing, biochemistry and brain imaging. In parallel, we are focusing on a set of mutations that we identified in genes related to the synapse (NLGN, SHANK, CNTN) by studying in depth their functional impact at the clinical and neuronal levels. Especially, we are exploring new ways of modulating the observed deficits by using human induced pluripotent stem cells (iPSC) and animal models. Our group is developing new methods for analyzing whole genome and brain imaging data as well as new paradigms for characterizing mouse social and vocal behaviors.
To fully understand living systems we need (i) experimental techniques to describe them as accurately and comprehensively as possible, and (ii) computational models able to predict their evolution from a given state and in response to external signals. The Imaging and Modeling Unit of Institut Pasteur, develops computational and experimental approaches to characterize and quantitatively predict selected cellular processes. Our current projects concentrate on : (i) investigating the dynamic spatial architecture of the genome and its functional consequences, and (ii) developing high resolution or high throughput imaging techniques, and applying them to study genome architecture and the cell biology of pathogens, especially HIV. Our lab mobilizes a spectrum of expertise including biophysics, microscopy, informatics and cell biology, and works in close collaboration with several experimental groups, many of them at Institut Pasteur.
The Mathematical Modelling of Infectious Diseases Unit at Institut Pasteur which is directed by Simon Cauchemez was created on November 1st 2013. The research focus of the Unit is to develop state-of-the-art mathematical and statistical methods to tackle the many challenges epidemiologists and microbiologists face when analysing infectious disease data. Our primary focus is the study of epidemics and outbreaks (for example, the emergence of Zika virus in the Americas, of MERS-CoV in the Middle East or of Ebola in West Africa). We aim to better understand how pathogens spread in human populations with a view to support policy making and optimize control strategies. These analyses benefit from a strong network of collaborators in the field (in particular within the large International Network of Pasteur Institutes) but also of strong connections with other excellence Centers in the field of mathematical modelling. Our secondary objective is to develop mathematical models that can be used to better characterize the infection process from experimental data. There is indeed a unique set of expertise and competences in microbiology at Institut Pasteur and we aim to develop innovative statistical and mathematical techniques to get more insights from the complex experimental data they generate. Our approach is therefore highly multidisciplinary, looking at infectious diseases through multiple perspectives (epidemiology, surveillance, Public Health, policy making, microbiology), multiple scales and multiple data streams.
Scientific activities of the Microbial Evolutionary Genomics Unit are centered on the bioinformatics and biostatistics analysis of genomes, at the crossroads of molecular evolution, population genetics, molecular epidemiology, and molecular genetics. We also develop some translational aspects related with the study of the diversity of bacterial pathogens. We focus on four major questions: How and why are genomes organized? How are such organizational features evolving in face of the extensive genome dynamics? What are the roles of mobile elements in the evolution of the host genomes? What is the interplay between genome dynamics and bacterial pathogen emergence at the microevolutionary and epidemiological scale?
The folding of chromosomes is a carefully regulated process, essential to the function and propagation of DNA molecule(s) over generations. Past and recent work have revealed its importance in bacteria or eukaryotes, where regulatory mechanisms have evolved to coordinate chromosome organization with other DNA-related metabolic processes such as segregation. Our research is focusing on the interplay between chromosome dynamics, cell cycle, and consequences on chromosome stability, that we study principally on microorganisms. To do so, we use a combination of genome-wide and single-cell technologies (3C, Hi-C, imaging), synthetic methods (neo-chromosome assembly), as well as in vitro and in vivo approaches. Among our recent results, we are reaching at a better understanding of the organizational changes experienced by yeast and bacterial genomes during replication and cell cycle, and how it is being influenced by metabolism (e.g. Marbouty et al., 2015; Guidi et al., 2015). We also have concomitantly developed computational techniques aiming at improving genome assembly and metagenomic/pan-genomic analysis through the exploitation of chromosome physical 3D signatures (Marbouty et al., 2014; Marie-Nelly et al., 2014a, 2014b). These so-called “contact genomics” approaches (Flot et al., 2015) have opened up new, unanticipated areas of research, which holds potential for both fundamental discoveries and biomedical applications. Our ongoing research projects include yeast chromosome dynamics during cell cycle; broadening our understanding of the regulation of chromosome organization and segregation in bacteria; influence of chromatin organization of meiotic double-strand break repair and mitotic genome stability. We also pursue our development of contact genomic applications, that we are now applying to a broad variety of questions, including metagenomic analysis, gene flow, and comparative genomics of complex genomes. Our projects usually involve collaborative efforts between geneticists, biophysicists and mathematicians, some in the lab and some through collaborations. We work in close collaboration with Dr. Julien Mozziconacci (UPMC, Paris) and Dr. Marcelo Nollmann (CBS, Montpellier). Check out our Github space for code and programs https://github.com/koszullab/
The enormous amount of genetic and genomic data generated in the last decade shows great promise in our ability to understand better human diseases and improving public health. Yet, the genetic architecture of complex human phenotypes remains elusive, and important questions are still unanswered. Our research addresses methodological issues related to the analysis of large multidimensional data in genetics and genomics. It focuses on particular on the development and application of innovative methods that aim at i) improving association mapping in large genomics datasets where multiple correlated variables are measured across multiple biological levels; ii) allowing for the robust evaluation of causal models that include both genetic, genomic, clinical and environmental data; and iii) identifying and targeting discoveries that have the highest potential clinical utility.
The structural bioinformatics research group was created in March 2001. Our research focuses on the relationship between sequence, three-dimensional structure, and function of proteins, using, among others, modelling techniques and molecular dynamics simulations. We also continue the development of software for automated NMR structure calculations, and are collaborating with structural genomics projects and the structure databases. We have established collaborations with experimental groups at the Institut Pasteur and elsewhere, for example, in atomic force spectroscopy, X-ray crystallography, and microscopy. The aim of our research is to complement structural studies (X-rays, NMR, Electron microscopy, and others) with in silico studies, to: better determine and predict three-dimensional structures better understand molecular recognition and molecular interactions. Our research topics include medically relevant molecular processes (infectious diseases, cancer, and the action of general anesthetics).