1. Enabling reactive microscopy with MicroMator
- Author
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Zachary R. Fox, Steven Fletcher, Achille Fraisse, Chetan Aditya, Sebastián Sosa-Carrillo, Julienne Petit, Sébastien Gilles, François Bertaux, Jakob Ruess, Gregory Batt, InBio - Méthodes Expérimentales et Computationnelles pour la Modélisation des Processus Cellulaires / Experimental and Computational Methods for Modeling Cellular Processes (INBIO), Institut Pasteur [Paris] (IP)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Paris Cité (UPCité), Los Alamos National Laboratory (LANL), Microbiologie structurale - Structural Microbiology (Microb. Struc. (UMR_3528 / U-Pasteur_5)), Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Service Expérimentation et Développement (SED [Saclay]), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This work was supported by ANR grants CyberCircuits (ANR‐18‐CE91‐0002), MEMIP (ANR‐16‐CE33‐0018), and Cogex (ANR‐16‐CE12‐0025), by the H2020 Fet‐Open COSY‐BIO grant (grant agreement no. 766840) and by the Inria IPL grant COSY. We acknowledge the support of the U.S. Department of Energy through the LANL/LDRD Program and the Center for Non Linear Studies for this work. We thank Anne-Marie Wehenkel for her detailed comments., ANR-18-CE91-0002,CyberCircuits,Circuits cybergénétiques pour tester la composabilité des réseaux génétiques(2018), ANR-16-CE33-0018,MEMIP,Modèles à effets mixtes de processus intracellulaires: méthodes, outils et applications(2016), ANR-16-CE12-0025,COGEX,Contrôle automatisé de l'expression des gènes(2016), and European Project: 766840,H2020,COSY-BIO(2017)
- Subjects
Machine Learning ,Microscopy ,Multidisciplinary ,Image Processing, Computer-Assisted ,General Physics and Astronomy ,Saccharomyces cerevisiae ,General Chemistry ,[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] ,Software ,General Biochemistry, Genetics and Molecular Biology - Abstract
Posté sur BioRxiv le 10 juin 2021; International audience; Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods and improved computational resources. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Applications in which reactiveness is needed are multifarious. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments. It provides a Python software environment and an extensible set of modules that greatly facilitate the definition of events with triggers and effects interacting with the experiment. We provide a pedagogic example performing dynamic adaptation of fluorescence illumination on bacteria, and demonstrate MicroMator’s potential via two challenging case studies in yeast to single-cell control and single-cell recombination, both requiring real-time tracking and light targeting at the single-cell level.
- Published
- 2022