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Counting biomolecules with Bayesian inference

Nature Computational Science volume 2, pages 74–75 (2022)Cite this article

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A combination of Bayesian inference, physics modeling, and Markov chain Monte Carlo sampling allows for accurate inference of biomolecule numbers and their photophysical state in cellular clusters.

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Fig. 1: An illustrative example of the Bayesian approach and the model architecture.

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  1. Decision and Bayesian Computation, Computational Biology & Neuroscience department, CNRS UMR 3751, Université de Paris, Institut Pasteur, Paris, France

    Jean-Baptiste Masson

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  1. Jean-Baptiste Masson
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Correspondence to Jean-Baptiste Masson.

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Masson, JB. Counting biomolecules with Bayesian inference. Nat Comput Sci 2, 74–75 (2022). https://doi.org/10.1038/s43588-022-00202-7

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