Units

Research Teams

Experimental and Computational Methods for Modeling Cellular

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).

Mathematical Modelling Of Infectious Diseases

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.

Spatial Regulation of Genomes

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/