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 Inria de Paris.