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The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast\n                    <jats:italic>Saccharomyces cerevisiae<\/jats:italic>\n                    SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010082","type":"journal-article","created":{"date-parts":[[2022,5,19]],"date-time":"2022-05-19T14:04:30Z","timestamp":1652969070000},"page":"e1010082","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":13,"title":["Scalable and flexible inference framework for stochastic dynamic single-cell models"],"prefix":"10.1371","volume":"18","author":[{"given":"Sebastian","family":"Persson","sequence":"first","affiliation":[]},{"given":"Niek","family":"Welkenhuysen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4641-3295","authenticated-orcid":true,"given":"Sviatlana","family":"Shashkova","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3756-438X","authenticated-orcid":true,"given":"Samuel","family":"Wiqvist","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0704-4810","authenticated-orcid":true,"given":"Patrick","family":"Reith","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1956-8985","authenticated-orcid":true,"given":"Gregor W.","family":"Schmidt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0732-9154","authenticated-orcid":true,"given":"Umberto","family":"Picchini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5142-5100","authenticated-orcid":true,"given":"Marija","family":"Cvijovic","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"issue":"4","key":"pcbi.1010082.ref001","doi-asserted-by":"crossref","first-page":"20170031","DOI":"10.1042\/BSR20170031","article-title":"Single-molecule fluorescence microscopy review: shedding new light on old problems","volume":"37","author":"S Shashkova","year":"2017","journal-title":"Bioscience Reports"},{"key":"pcbi.1010082.ref002","doi-asserted-by":"crossref","unstructured":"Hunting down heterogeneity; 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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo; 2014."},{"key":"pcbi.1010082.ref058","first-page":"1682","volume-title":"International Conference on Artificial Intelligence and Statistics, {AISTATS} 2018, 9-11 April 2018","author":"H Ge","year":"2018"},{"issue":"2","key":"pcbi.1010082.ref059","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1214\/07-AOS574","article-title":"The pseudo-marginal approach for efficient Monte Carlo computations","volume":"37","author":"C Andrieu","year":"2009","journal-title":"Annals of Statistics"},{"key":"pcbi.1010082.ref060","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-3437-9","volume-title":"Sequential Monte Carlo Methods in Practice","author":"A Doucet","year":"2001"},{"key":"pcbi.1010082.ref061","volume-title":"Genealogical and interactin g particle systems, with applications","author":"P del Moral","year":"2004"},{"key":"pcbi.1010082.ref062","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.jeconom.2012.06.004","article-title":"On some properties of Markov chain Monte Carlo simulation methods based on the particle filter","volume":"vol. 171","author":"MK Pitt","year":"2012","journal-title":"Journal of Econometrics"},{"issue":"1","key":"pcbi.1010082.ref063","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1093\/biomet\/asaa044","article-title":"Large-sample asymptotics of the pseudo-marginal method","volume":"108","author":"SM Schmon","year":"2021","journal-title":"Biometrika"},{"issue":"1","key":"pcbi.1010082.ref064","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1214\/14-AOS1278","article-title":"On the efficiency of pseudo-marginal random walk metropolis algorithms","volume":"43","author":"C Sherlock","year":"2015","journal-title":"Annals of Statistics"},{"issue":"2","key":"pcbi.1010082.ref065","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1093\/biomet\/asu075","article-title":"Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator","volume":"102","author":"A Doucet","year":"2015","journal-title":"Biometrika"},{"key":"pcbi.1010082.ref066","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.csda.2019.01.006","article-title":"Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models","volume":"136","author":"A Golightly","year":"2019","journal-title":"Computational Statistics and Data Analysis"},{"issue":"3","key":"pcbi.1010082.ref067","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1111\/j.1541-0420.2005.00345.x","article-title":"Bayesian inference for stochastic kinetic models using a diffusion approximation","volume":"61","author":"A Golightly","year":"2005","journal-title":"Biometrics"},{"key":"pcbi.1010082.ref068","unstructured":"Lixoft. 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Antony, France: Lixoft SAS; http:\/\/lixoft.com\/products\/monolix\/. 2019;."},{"issue":"1","key":"pcbi.1010082.ref069","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5334\/jors.151","article-title":"DifferentialEquations.jl\u2014A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia","volume":"5","author":"C Rackauckas","year":"2017","journal-title":"Journal of Open Research Software"},{"issue":"1","key":"pcbi.1010082.ref070","doi-asserted-by":"crossref","first-page":"14.22.1","DOI":"10.1002\/0471142727.mb1422s101","article-title":"Using CellX to Quantify Intracellular Events","volume":"101","author":"C Mayer","year":"2013","journal-title":"Current Protocols in Molecular Biology"},{"issue":"28","key":"pcbi.1010082.ref071","doi-asserted-by":"crossref","first-page":"11403","DOI":"10.1073\/pnas.1215850110","article-title":"Dissecting genealogy and cell cycle as sources of cell-to-cell variability in MAPK signaling using high-throughput lineage tracking","volume":"110","author":"M Ricicova","year":"2013","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"issue":"8","key":"pcbi.1010082.ref072","doi-asserted-by":"crossref","first-page":"4144","DOI":"10.1021\/ac504611t","article-title":"Versatile, simple-to-use microfluidic cell-culturing chip for long-term, high-resolution, time-lapse imaging","volume":"87","author":"O Frey","year":"2015","journal-title":"Analytical Chemistry"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010082","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010082","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T17:13:33Z","timestamp":1700586813000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010082"}},"subtitle":[],"editor":[{"given":"James R.","family":"Faeder","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,5,19]]},"references-count":72,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5,19]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010082","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.07.01.450748","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,19]]}}}