{"title":"Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"CaGu"}],"issue":"3","citation":{"apa":"Davidović, A., Chait, R. P., Batt, G., &#38; Ruess, J. (2022). Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">https://doi.org/10.1371/journal.pcbi.1009950</a>","mla":"Davidović, Anđela, et al. “Parameter Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised at the Single Cell Level.” <i>PLoS Computational Biology</i>, vol. 18, no. 3, e1009950, Public Library of Science, 2022, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">10.1371/journal.pcbi.1009950</a>.","ieee":"A. Davidović, R. P. Chait, G. Batt, and J. Ruess, “Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level,” <i>PLoS Computational Biology</i>, vol. 18, no. 3. Public Library of Science, 2022.","ama":"Davidović A, Chait RP, Batt G, Ruess J. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. <i>PLoS Computational Biology</i>. 2022;18(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">10.1371/journal.pcbi.1009950</a>","ista":"Davidović A, Chait RP, Batt G, Ruess J. 2022. Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. PLoS Computational Biology. 18(3), e1009950.","chicago":"Davidović, Anđela, Remy P Chait, Gregory Batt, and Jakob Ruess. “Parameter Inference for Stochastic Biochemical Models from Perturbation Experiments Parallelised at the Single Cell Level.” <i>PLoS Computational Biology</i>. Public Library of Science, 2022. <a href=\"https://doi.org/10.1371/journal.pcbi.1009950\">https://doi.org/10.1371/journal.pcbi.1009950</a>.","short":"A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18 (2022)."},"_id":"10939","month":"03","article_type":"original","year":"2022","scopus_import":"1","ddc":["570","000"],"volume":18,"publication":"PLoS Computational Biology","oa_version":"Published Version","status":"public","author":[{"last_name":"Davidović","first_name":"Anđela","full_name":"Davidović, Anđela"},{"first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","last_name":"Chait","orcid":"0000-0003-0876-3187","full_name":"Chait, Remy P"},{"last_name":"Batt","first_name":"Gregory","full_name":"Batt, Gregory"},{"first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob"}],"type":"journal_article","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"intvolume":"        18","publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"related_material":{"link":[{"relation":"software","url":"https://gitlab.pasteur.fr/adavidov/inferencelnakf"}]},"date_updated":"2022-04-04T10:21:53Z","date_published":"2022-03-18T00:00:00Z","publication_status":"published","quality_controlled":"1","article_number":"e1009950","day":"18","has_accepted_license":"1","article_processing_charge":"No","acknowledgement":"We thank Virgile Andreani for useful discussions about the model and parameter inference. We thank Johan Paulsson and Jeffrey J Tabor for kind gifts of plasmids. R was supported by the ANR grant CyberCircuits (ANR-18-CE91-0002). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","abstract":[{"text":"Understanding and characterising biochemical processes inside single cells requires experimental platforms that allow one to perturb and observe the dynamics of such processes as well as computational methods to build and parameterise models from the collected data. Recent progress with experimental platforms and optogenetics has made it possible to expose each cell in an experiment to an individualised input and automatically record cellular responses over days with fine time resolution. However, methods to infer parameters of stochastic kinetic models from single-cell longitudinal data have generally been developed under the assumption that experimental data is sparse and that responses of cells to at most a few different input perturbations can be observed. Here, we investigate and compare different approaches for calculating parameter likelihoods of single-cell longitudinal data based on approximations of the chemical master equation (CME) with a particular focus on coupling the linear noise approximation (LNA) or moment closure methods to a Kalman filter. We show that, as long as cells are measured sufficiently frequently, coupling the LNA to a Kalman filter allows one to accurately approximate likelihoods and to infer model parameters from data even in cases where the LNA provides poor approximations of the CME. Furthermore, the computational cost of filtering-based iterative likelihood evaluation scales advantageously in the number of measurement times and different input perturbations and is thus ideally suited for data obtained from modern experimental platforms. To demonstrate the practical usefulness of these results, we perform an experiment in which single cells, equipped with an optogenetic gene expression system, are exposed to various different light-input sequences and measured at several hundred time points and use parameter inference based on iterative likelihood evaluation to parameterise a stochastic model of the system.","lang":"eng"}],"language":[{"iso":"eng"}],"date_created":"2022-04-03T22:01:42Z","oa":1,"publisher":"Public Library of Science","file_date_updated":"2022-04-04T10:14:39Z","file":[{"date_created":"2022-04-04T10:14:39Z","file_size":2958642,"date_updated":"2022-04-04T10:14:39Z","creator":"dernst","checksum":"458ef542761fb714ced214f240daf6b2","relation":"main_file","access_level":"open_access","success":1,"content_type":"application/pdf","file_name":"2022_PLoSCompBio_Davidovic.pdf","file_id":"10947"}],"doi":"10.1371/journal.pcbi.1009950"}