[{"file_date_updated":"2022-04-04T10:14:39Z","quality_controlled":"1","article_type":"original","publisher":"Public Library of Science","author":[{"full_name":"Davidović, Anđela","first_name":"Anđela","last_name":"Davidović"},{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","last_name":"Chait","first_name":"Remy P"},{"full_name":"Batt, Gregory","last_name":"Batt","first_name":"Gregory"},{"full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess","first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"}],"issue":"3","_id":"10939","scopus_import":"1","title":"Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level","intvolume":"        18","publication_status":"published","department":[{"_id":"CaGu"}],"date_created":"2022-04-03T22:01:42Z","article_processing_charge":"No","ddc":["570","000"],"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.","volume":18,"date_updated":"2022-04-04T10:21:53Z","citation":{"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>","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>","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>.","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.","short":"A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18 (2022).","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>.","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."},"year":"2022","abstract":[{"lang":"eng","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."}],"doi":"10.1371/journal.pcbi.1009950","day":"18","language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","has_accepted_license":"1","month":"03","article_number":"e1009950","oa_version":"Published Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","related_material":{"link":[{"relation":"software","url":"https://gitlab.pasteur.fr/adavidov/inferencelnakf"}]},"file":[{"access_level":"open_access","relation":"main_file","success":1,"creator":"dernst","file_id":"10947","file_size":2958642,"checksum":"458ef542761fb714ced214f240daf6b2","date_created":"2022-04-04T10:14:39Z","file_name":"2022_PLoSCompBio_Davidovic.pdf","content_type":"application/pdf","date_updated":"2022-04-04T10:14:39Z"}],"date_published":"2022-03-18T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]}},{"quality_controlled":"1","file_date_updated":"2022-05-16T08:53:11Z","publisher":"Public Library of Science","article_type":"original","pmid":1,"_id":"10535","scopus_import":"1","author":[{"id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","full_name":"Bod'ová, Katarína","orcid":"0000-0002-7214-0171","last_name":"Bod'ová","first_name":"Katarína"},{"id":"485BB5A4-F248-11E8-B48F-1D18A9856A87","last_name":"Szep","first_name":"Eniko","full_name":"Szep, Eniko"},{"full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"}],"issue":"12","publication_status":"published","article_processing_charge":"No","date_created":"2021-12-12T23:01:27Z","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"title":"Dynamic maximum entropy provides accurate approximation of structured population dynamics","intvolume":"        17","volume":17,"acknowledgement":"Computational resources for the study were provided by the Institute of Science and Technology, Austria.\r\nKB received funding from the Scientific Grant Agency of the Slovak Republic under the Grants Nos. 1/0755/19 and 1/0521/20.","ddc":["570"],"date_updated":"2022-08-01T10:48:04Z","citation":{"mla":"Bodova, Katarina, et al. “Dynamic Maximum Entropy Provides Accurate Approximation of Structured Population Dynamics.” <i>PLoS Computational Biology</i>, vol. 17, no. 12, e1009661, Public Library of Science, 2021, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">10.1371/journal.pcbi.1009661</a>.","short":"K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021).","ista":"Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate approximation of structured population dynamics. PLoS Computational Biology. 17(12), e1009661.","ama":"Bodova K, Szep E, Barton NH. Dynamic maximum entropy provides accurate approximation of structured population dynamics. <i>PLoS Computational Biology</i>. 2021;17(12). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">10.1371/journal.pcbi.1009661</a>","apa":"Bodova, K., Szep, E., &#38; Barton, N. H. (2021). Dynamic maximum entropy provides accurate approximation of structured population dynamics. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">https://doi.org/10.1371/journal.pcbi.1009661</a>","ieee":"K. Bodova, E. Szep, and N. H. Barton, “Dynamic maximum entropy provides accurate approximation of structured population dynamics,” <i>PLoS Computational Biology</i>, vol. 17, no. 12. Public Library of Science, 2021.","chicago":"Bodova, Katarina, Eniko Szep, and Nicholas H Barton. “Dynamic Maximum Entropy Provides Accurate Approximation of Structured Population Dynamics.” <i>PLoS Computational Biology</i>. Public Library of Science, 2021. <a href=\"https://doi.org/10.1371/journal.pcbi.1009661\">https://doi.org/10.1371/journal.pcbi.1009661</a>."},"year":"2021","external_id":{"pmid":["34851948"],"arxiv":["2102.03669"]},"arxiv":1,"doi":"10.1371/journal.pcbi.1009661","day":"01","abstract":[{"text":"Realistic models of biological processes typically involve interacting components on multiple scales, driven by changing environment and inherent stochasticity. Such models are often analytically and numerically intractable. We revisit a dynamic maximum entropy method that combines a static maximum entropy with a quasi-stationary approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics, without the need to track microscopic details. Although the method has been previously applied to a few (rather complicated) applications in population genetics, our main goal here is to explain and to better understand how the method works. We demonstrate the usefulness of the method for two widely studied stochastic problems, highlighting its accuracy in capturing important macroscopic quantities even in rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process, the method recovers the exact dynamics whilst for a stochastic island model with migration from other habitats, the approximation retains high macroscopic accuracy under a wide range of scenarios in a dynamic environment.","lang":"eng"}],"language":[{"iso":"eng"}],"publication":"PLoS Computational Biology","has_accepted_license":"1","acknowledged_ssus":[{"_id":"ScienComp"}],"oa_version":"Published Version","month":"12","article_number":"e1009661","file":[{"file_name":"2021_PLOsComBio_Bodova.pdf","content_type":"application/pdf","date_updated":"2022-05-16T08:53:11Z","file_size":2299486,"checksum":"dcd185d4f7e0acee25edf1d6537f447e","date_created":"2022-05-16T08:53:11Z","creator":"dernst","file_id":"11383","success":1,"access_level":"open_access","relation":"main_file"}],"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"date_published":"2021-12-01T00:00:00Z","type":"journal_article","publication_identifier":{"eissn":["1553-7358"],"issn":["1553-734X"]},"oa":1},{"language":[{"iso":"eng"}],"keyword":["Ecology","Modelling and Simulation","Computational Theory and Mathematics","Genetics","Ecology","Evolution","Behavior and Systematics","Molecular Biology","Cellular and Molecular Neuroscience"],"publication":"PLOS Computational Biology","has_accepted_license":"1","month":"11","article_number":"e1008402","oa_version":"Published Version","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","status":"public","file":[{"content_type":"application/pdf","file_name":"2020_PlosCompBio_Kaveh.pdf","date_updated":"2020-11-18T07:26:10Z","checksum":"555456dd0e47bcf9e0994bcb95577e88","file_size":2498594,"date_created":"2020-11-18T07:26:10Z","creator":"dernst","file_id":"8768","access_level":"open_access","success":1,"relation":"main_file"}],"date_published":"2020-11-05T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"issn":["1553-734X"],"eissn":["1553-7358"]},"file_date_updated":"2020-11-18T07:26:10Z","quality_controlled":"1","article_type":"original","publisher":"Public Library of Science","author":[{"full_name":"Kaveh, Kamran","first_name":"Kamran","last_name":"Kaveh"},{"full_name":"McAvoy, Alex","last_name":"McAvoy","first_name":"Alex"},{"id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4561-241X","full_name":"Chatterjee, Krishnendu","first_name":"Krishnendu","last_name":"Chatterjee"},{"full_name":"Nowak, Martin A.","first_name":"Martin A.","last_name":"Nowak"}],"issue":"11","_id":"8767","scopus_import":"1","title":"The Moran process on 2-chromatic graphs","intvolume":"        16","publication_status":"published","date_created":"2020-11-18T07:20:23Z","article_processing_charge":"No","department":[{"_id":"KrCh"}],"ddc":["000"],"acknowledgement":"We thank Igor Erovenko for many helpful comments on an earlier version of this paper. : Army Research Laboratory (grant W911NF-18-2-0265) (M.A.N.); the Bill & Melinda Gates Foundation (grant OPP1148627) (M.A.N.); the NVIDIA Corporation (A.M.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","volume":16,"isi":1,"external_id":{"isi":["000591317200004"]},"date_updated":"2023-08-22T12:49:18Z","year":"2020","citation":{"ista":"Kaveh K, McAvoy A, Chatterjee K, Nowak MA. 2020. The Moran process on 2-chromatic graphs. PLOS Computational Biology. 16(11), e1008402.","short":"K. Kaveh, A. McAvoy, K. Chatterjee, M.A. Nowak, PLOS Computational Biology 16 (2020).","mla":"Kaveh, Kamran, et al. “The Moran Process on 2-Chromatic Graphs.” <i>PLOS Computational Biology</i>, vol. 16, no. 11, e1008402, Public Library of Science, 2020, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008402\">10.1371/journal.pcbi.1008402</a>.","ieee":"K. Kaveh, A. McAvoy, K. Chatterjee, and M. A. Nowak, “The Moran process on 2-chromatic graphs,” <i>PLOS Computational Biology</i>, vol. 16, no. 11. Public Library of Science, 2020.","chicago":"Kaveh, Kamran, Alex McAvoy, Krishnendu Chatterjee, and Martin A. Nowak. “The Moran Process on 2-Chromatic Graphs.” <i>PLOS Computational Biology</i>. Public Library of Science, 2020. <a href=\"https://doi.org/10.1371/journal.pcbi.1008402\">https://doi.org/10.1371/journal.pcbi.1008402</a>.","ama":"Kaveh K, McAvoy A, Chatterjee K, Nowak MA. The Moran process on 2-chromatic graphs. <i>PLOS Computational Biology</i>. 2020;16(11). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1008402\">10.1371/journal.pcbi.1008402</a>","apa":"Kaveh, K., McAvoy, A., Chatterjee, K., &#38; Nowak, M. A. (2020). The Moran process on 2-chromatic graphs. <i>PLOS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1008402\">https://doi.org/10.1371/journal.pcbi.1008402</a>"},"abstract":[{"lang":"eng","text":"Resources are rarely distributed uniformly within a population. Heterogeneity in the concentration of a drug, the quality of breeding sites, or wealth can all affect evolutionary dynamics. In this study, we represent a collection of properties affecting the fitness at a given location using a color. A green node is rich in resources while a red node is poorer. More colors can represent a broader spectrum of resource qualities. For a population evolving according to the birth-death Moran model, the first question we address is which structures, identified by graph connectivity and graph coloring, are evolutionarily equivalent. We prove that all properly two-colored, undirected, regular graphs are evolutionarily equivalent (where “properly colored” means that no two neighbors have the same color). We then compare the effects of background heterogeneity on properly two-colored graphs to those with alternative schemes in which the colors are permuted. Finally, we discuss dynamic coloring as a model for spatiotemporal resource fluctuations, and we illustrate that random dynamic colorings often diminish the effects of background heterogeneity relative to a proper two-coloring."}],"doi":"10.1371/journal.pcbi.1008402","day":"05"}]