Contributors: Décision et processus Bayesiens - Decision and Bayesian Computation; Institut Pasteur Paris (IP)-Centre National de la Recherche Scientifique (CNRS); Institut de Recherche en Infectiologie de Montpellier (IRIM); Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS); Laboratoire Charles Fabry / Biophotonique; Laboratoire Charles Fabry (LCF); Institut d'Optique Graduate School (IOGS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut d'Optique Graduate School (IOGS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS); This study was funded by the Institut Pasteur, L’ Agence Nationale de la Recherche (TRamWAy, ANR-17-CE23-0016), the INCEPTION project (PIA/ANR-16-CONV-0005, OG), and the programme d’investissement d’avenir supported by L’ Agence Nationale de la Recherche ANR-19-P3IA-0001. The funding sources had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.; We thank Aleksandra Walczak, Vincent Hakim, Bassam Hajj, Mathieu Coppey and Maxime Dahan (†) for helpful discussions.; ANR-17-CE23-0016,TRamWAy,L'Analyseur Automatique de Marches Aléatoires Biomoléculaires: La Science des molécules uniques à l'époque des données massives(2017); ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016); ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019)
نبذة مختصرة : International audience ; We devise a method to detect and estimate forces in a heterogeneous environment based on experimentally recorded stochastic trajectories. In particular, we focus on systems modeled by the heterogeneous overdamped Langevin equation. Here, the observed drift includes a "spurious" force term when the diffusivity varies in space. We show how Bayesian inference can be leveraged to reliably infer forces by taking into account such spurious forces of unknown amplitude as well as experimental sources of error. The method is based on marginalizing the force posterior over all possible spurious force contributions. The approach is combined with a Bayes factor statistical test for the presence of forces. The performance of our method is investigated analytically, numerically and tested on experimental data sets. The main results are obtained in a closed form allowing for direct exploration of their properties and fast computation. The method is incorporated into TRamWAy, an open-source software platform for automated analysis of biomolecule trajectories.
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