[{"date_created":"2022-04-03T22:01:42Z","article_type":"original","volume":18,"title":"Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level","oa_version":"Published Version","day":"18","scopus_import":"1","author":[{"last_name":"Davidović","full_name":"Davidović, Anđela","first_name":"Anđela"},{"first_name":"Remy P","orcid":"0000-0003-0876-3187","last_name":"Chait","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","full_name":"Chait, Remy P"},{"first_name":"Gregory","full_name":"Batt, Gregory","last_name":"Batt"},{"first_name":"Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob"}],"file_date_updated":"2022-04-04T10:14:39Z","publication_identifier":{"eissn":["1553-7358"],"issn":["1553-734X"]},"publication_status":"published","intvolume":"        18","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"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"}],"has_accepted_license":"1","article_number":"e1009950","file":[{"success":1,"file_name":"2022_PLoSCompBio_Davidovic.pdf","access_level":"open_access","content_type":"application/pdf","relation":"main_file","checksum":"458ef542761fb714ced214f240daf6b2","date_created":"2022-04-04T10:14:39Z","file_size":2958642,"creator":"dernst","date_updated":"2022-04-04T10:14:39Z","file_id":"10947"}],"department":[{"_id":"CaGu"}],"month":"03","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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>.","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.","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>.","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>","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>","short":"A. Davidović, R.P. Chait, G. Batt, J. Ruess, PLoS Computational Biology 18 (2022).","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."},"issue":"3","language":[{"iso":"eng"}],"oa":1,"type":"journal_article","_id":"10939","date_updated":"2022-04-04T10:21:53Z","publisher":"Public Library of Science","article_processing_charge":"No","doi":"10.1371/journal.pcbi.1009950","quality_controlled":"1","ddc":["570","000"],"related_material":{"link":[{"url":"https://gitlab.pasteur.fr/adavidov/inferencelnakf","relation":"software"}]},"year":"2022","date_published":"2022-03-18T00:00:00Z","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.","status":"public","publication":"PLoS Computational Biology"},{"related_material":{"record":[{"status":"public","relation":"research_data","id":"9786"}]},"external_id":{"isi":["000481577700032"]},"year":"2019","isi":1,"date_published":"2019-07-02T00:00:00Z","status":"public","publication":"PLoS Computational Biology","project":[{"name":"Effects of Stochasticity on the Function of Restriction-Modi cation Systems at the Single-Cell Level","grant_number":"24210","_id":"251D65D8-B435-11E9-9278-68D0E5697425"},{"_id":"251BCBEC-B435-11E9-9278-68D0E5697425","name":"Multi-Level Conflicts in Evolutionary Dynamics of Restriction-Modification Systems","grant_number":"RGY0079/2011"}],"type":"journal_article","_id":"6784","date_updated":"2023-08-29T07:10:06Z","publisher":"Public Library of Science","article_processing_charge":"No","doi":"10.1371/journal.pcbi.1007168","quality_controlled":"1","ddc":["570"],"article_number":"e1007168","file":[{"relation":"main_file","checksum":"7ded4721b41c2a0fc66a1c634540416a","file_name":"2019_PlosComputBiology_Ruess.pdf","content_type":"application/pdf","access_level":"open_access","file_id":"6803","file_size":2200003,"date_created":"2019-08-12T12:27:26Z","creator":"dernst","date_updated":"2020-07-14T12:47:40Z"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"month":"07","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","citation":{"apa":"Ruess, J., Pleska, M., Guet, C. C., &#38; Tkačik, G. (2019). Molecular noise of innate immunity shapes bacteria-phage ecologies. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">https://doi.org/10.1371/journal.pcbi.1007168</a>","mla":"Ruess, Jakob, et al. “Molecular Noise of Innate Immunity Shapes Bacteria-Phage Ecologies.” <i>PLoS Computational Biology</i>, vol. 15, no. 7, e1007168, Public Library of Science, 2019, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">10.1371/journal.pcbi.1007168</a>.","chicago":"Ruess, Jakob, Maros Pleska, Calin C Guet, and Gašper Tkačik. “Molecular Noise of Innate Immunity Shapes Bacteria-Phage Ecologies.” <i>PLoS Computational Biology</i>. Public Library of Science, 2019. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">https://doi.org/10.1371/journal.pcbi.1007168</a>.","ista":"Ruess J, Pleska M, Guet CC, Tkačik G. 2019. Molecular noise of innate immunity shapes bacteria-phage ecologies. PLoS Computational Biology. 15(7), e1007168.","ieee":"J. Ruess, M. Pleska, C. C. Guet, and G. Tkačik, “Molecular noise of innate immunity shapes bacteria-phage ecologies,” <i>PLoS Computational Biology</i>, vol. 15, no. 7. Public Library of Science, 2019.","short":"J. Ruess, M. Pleska, C.C. Guet, G. Tkačik, PLoS Computational Biology 15 (2019).","ama":"Ruess J, Pleska M, Guet CC, Tkačik G. Molecular noise of innate immunity shapes bacteria-phage ecologies. <i>PLoS Computational Biology</i>. 2019;15(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">10.1371/journal.pcbi.1007168</a>"},"issue":"7","language":[{"iso":"eng"}],"oa":1,"date_created":"2019-08-11T21:59:19Z","article_type":"original","volume":15,"oa_version":"Published Version","title":"Molecular noise of innate immunity shapes bacteria-phage ecologies","scopus_import":"1","day":"02","author":[{"orcid":"0000-0003-1615-3282","first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob","last_name":"Ruess"},{"full_name":"Pleska, Maros","id":"4569785E-F248-11E8-B48F-1D18A9856A87","last_name":"Pleska","first_name":"Maros","orcid":"0000-0001-7460-7479"},{"full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","last_name":"Guet","first_name":"Calin C","orcid":"0000-0001-6220-2052"},{"first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper"}],"file_date_updated":"2020-07-14T12:47:40Z","publication_identifier":{"eissn":["1553-7358"]},"publication_status":"published","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"lang":"eng","text":"Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro- and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. Here, we employ such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We show analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems."}],"intvolume":"        15","has_accepted_license":"1"},{"status":"public","citation":{"short":"J. Ruess, M. Pleska, C.C. Guet, G. Tkačik, (2019).","ieee":"J. Ruess, M. Pleska, C. C. Guet, and G. Tkačik, “Supporting text and results.” Public Library of Science, 2019.","ama":"Ruess J, Pleska M, Guet CC, Tkačik G. Supporting text and results. 2019. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">10.1371/journal.pcbi.1007168.s001</a>","mla":"Ruess, Jakob, et al. <i>Supporting Text and Results</i>. Public Library of Science, 2019, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">10.1371/journal.pcbi.1007168.s001</a>.","apa":"Ruess, J., Pleska, M., Guet, C. C., &#38; Tkačik, G. (2019). Supporting text and results. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">https://doi.org/10.1371/journal.pcbi.1007168.s001</a>","chicago":"Ruess, Jakob, Maros Pleska, Calin C Guet, and Gašper Tkačik. “Supporting Text and Results.” Public Library of Science, 2019. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">https://doi.org/10.1371/journal.pcbi.1007168.s001</a>.","ista":"Ruess J, Pleska M, Guet CC, Tkačik G. 2019. Supporting text and results, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">10.1371/journal.pcbi.1007168.s001</a>."},"date_published":"2019-07-02T00:00:00Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","year":"2019","month":"07","related_material":{"record":[{"id":"6784","status":"public","relation":"used_in_publication"}]},"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"author":[{"orcid":"0000-0003-1615-3282","first_name":"Jakob","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob"},{"last_name":"Pleska","full_name":"Pleska, Maros","id":"4569785E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-7460-7479","first_name":"Maros"},{"last_name":"Guet","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","full_name":"Guet, Calin C","first_name":"Calin C","orcid":"0000-0001-6220-2052"},{"orcid":"0000-0002-6699-1455","first_name":"Gašper","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik"}],"doi":"10.1371/journal.pcbi.1007168.s001","article_processing_charge":"No","day":"02","publisher":"Public Library of Science","oa_version":"Published Version","title":"Supporting text and results","date_updated":"2023-08-29T07:10:05Z","_id":"9786","type":"research_data_reference","date_created":"2021-08-06T08:23:43Z"},{"article_number":"1535","file":[{"access_level":"open_access","content_type":"application/pdf","file_name":"IST-2017-911-v1+1_s41467-017-01683-1.pdf","checksum":"44bb5d0229926c23a9955d9fe0f9723f","relation":"main_file","date_updated":"2020-07-14T12:47:20Z","creator":"system","date_created":"2018-12-12T10:16:05Z","file_size":1951699,"file_id":"5190"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"month":"12","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Chait RP, Ruess J, Bergmiller T, Tkačik G, Guet CC. 2017. Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. 8(1), 1535.","chicago":"Chait, Remy P, Jakob Ruess, Tobias Bergmiller, Gašper Tkačik, and Calin C Guet. “Shaping Bacterial Population Behavior through Computer Interfaced Control of Individual Cells.” <i>Nature Communications</i>. Nature Publishing Group, 2017. <a href=\"https://doi.org/10.1038/s41467-017-01683-1\">https://doi.org/10.1038/s41467-017-01683-1</a>.","mla":"Chait, Remy P., et al. “Shaping Bacterial Population Behavior through Computer Interfaced Control of Individual Cells.” <i>Nature Communications</i>, vol. 8, no. 1, 1535, Nature Publishing Group, 2017, doi:<a href=\"https://doi.org/10.1038/s41467-017-01683-1\">10.1038/s41467-017-01683-1</a>.","apa":"Chait, R. P., Ruess, J., Bergmiller, T., Tkačik, G., &#38; Guet, C. C. (2017). Shaping bacterial population behavior through computer interfaced control of individual cells. <i>Nature Communications</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/s41467-017-01683-1\">https://doi.org/10.1038/s41467-017-01683-1</a>","ama":"Chait RP, Ruess J, Bergmiller T, Tkačik G, Guet CC. Shaping bacterial population behavior through computer interfaced control of individual cells. <i>Nature Communications</i>. 2017;8(1). doi:<a href=\"https://doi.org/10.1038/s41467-017-01683-1\">10.1038/s41467-017-01683-1</a>","short":"R.P. Chait, J. Ruess, T. Bergmiller, G. Tkačik, C.C. Guet, Nature Communications 8 (2017).","ieee":"R. P. Chait, J. Ruess, T. Bergmiller, G. Tkačik, and C. C. Guet, “Shaping bacterial population behavior through computer interfaced control of individual cells,” <i>Nature Communications</i>, vol. 8, no. 1. Nature Publishing Group, 2017."},"issue":"1","language":[{"iso":"eng"}],"pubrep_id":"911","oa":1,"date_created":"2018-12-11T11:47:30Z","volume":8,"title":"Shaping bacterial population behavior through computer interfaced control of individual cells","oa_version":"Published Version","scopus_import":1,"day":"01","author":[{"full_name":"Chait, Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","last_name":"Chait","first_name":"Remy P","orcid":"0000-0003-0876-3187"},{"full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","first_name":"Jakob","orcid":"0000-0003-1615-3282"},{"last_name":"Bergmiller","full_name":"Bergmiller, Tobias","id":"2C471CFA-F248-11E8-B48F-1D18A9856A87","first_name":"Tobias","orcid":"0000-0001-5396-4346"},{"first_name":"Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Guet","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6220-2052","first_name":"Calin C"}],"file_date_updated":"2020-07-14T12:47:20Z","publication_identifier":{"issn":["20411723"]},"publication_status":"published","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"text":"Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior.","lang":"eng"}],"intvolume":"         8","has_accepted_license":"1","publist_id":"7191","year":"2017","acknowledgement":"We are grateful to M. Lang, H. Janovjak, M. Khammash, A. Milias-Argeitis, M. Rullan, G. Batt, A. Bosma-Moody, Aryan, S. Leibler, and members of the Guet and Tkačik groups for helpful discussion, comments, and suggestions. We thank A. Moglich, T. Mathes, J. Tabor, and S. Schmidl for kind gifts of strains, and R. Hauschild, B. Knep, M. Lang, T. Asenov, E. Papusheva, T. Menner, T. Adletzberger, and J. Merrin for technical assistance. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. [291734]. (to R.C. and J.R.), Austrian Science Fund grant FWF P28844 (to G.T.), and internal IST Austria Interdisciplinary Project Support. J.R. acknowledges support from the Agence Nationale de la Recherche (ANR) under Grant Nos. ANR-16-CE33-0018 (MEMIP), ANR-16-CE12-0025 (COGEX) and ANR-10-BINF-06-01 (ICEBERG).","date_published":"2017-12-01T00:00:00Z","ec_funded":1,"status":"public","publication":"Nature Communications","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"},{"call_identifier":"FWF","grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"type":"journal_article","_id":"613","date_updated":"2021-01-12T08:06:15Z","publisher":"Nature Publishing Group","article_processing_charge":"Yes (in subscription journal)","doi":"10.1038/s41467-017-01683-1","quality_controlled":"1","ddc":["576","579"]},{"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"month":"11","citation":{"ieee":"C. Schilling, S. Bogomolov, T. A. Henzinger, A. Podelski, and J. Ruess, “Adaptive moment closure for parameter inference of biochemical reaction networks,” <i>Biosystems</i>, vol. 149. Elsevier, pp. 15–25, 2016.","short":"C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems 149 (2016) 15–25.","ama":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. Adaptive moment closure for parameter inference of biochemical reaction networks. <i>Biosystems</i>. 2016;149:15-25. doi:<a href=\"https://doi.org/10.1016/j.biosystems.2016.07.005\">10.1016/j.biosystems.2016.07.005</a>","apa":"Schilling, C., Bogomolov, S., Henzinger, T. A., Podelski, A., &#38; Ruess, J. (2016). Adaptive moment closure for parameter inference of biochemical reaction networks. <i>Biosystems</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.biosystems.2016.07.005\">https://doi.org/10.1016/j.biosystems.2016.07.005</a>","mla":"Schilling, Christian, et al. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” <i>Biosystems</i>, vol. 149, Elsevier, 2016, pp. 15–25, doi:<a href=\"https://doi.org/10.1016/j.biosystems.2016.07.005\">10.1016/j.biosystems.2016.07.005</a>.","ista":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. 2016. Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. 149, 15–25.","chicago":"Schilling, Christian, Sergiy Bogomolov, Thomas A Henzinger, Andreas Podelski, and Jakob Ruess. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” <i>Biosystems</i>. Elsevier, 2016. <a href=\"https://doi.org/10.1016/j.biosystems.2016.07.005\">https://doi.org/10.1016/j.biosystems.2016.07.005</a>."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","language":[{"iso":"eng"}],"volume":149,"date_created":"2018-12-11T11:50:24Z","author":[{"first_name":"Christian","full_name":"Schilling, Christian","last_name":"Schilling"},{"id":"369D9A44-F248-11E8-B48F-1D18A9856A87","full_name":"Bogomolov, Sergiy","last_name":"Bogomolov","first_name":"Sergiy","orcid":"0000-0002-0686-0365"},{"last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","full_name":"Henzinger, Thomas A","first_name":"Thomas A","orcid":"0000−0002−2985−7724"},{"last_name":"Podelski","full_name":"Podelski, Andreas","first_name":"Andreas"},{"first_name":"Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob"}],"scopus_import":1,"day":"01","title":"Adaptive moment closure for parameter inference of biochemical reaction networks","oa_version":"None","publication_status":"published","abstract":[{"lang":"eng","text":"Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space. © 2016 Elsevier Ireland Ltd"}],"intvolume":"       149","publist_id":"6210","year":"2016","related_material":{"record":[{"id":"1658","relation":"earlier_version","status":"public"}]},"ec_funded":1,"acknowledgement":"This work is based on the CMSB 2015 paper “Adaptive moment closure for parameter inference of biochemical reaction networks” (Bogomolov et al., 2015). The work was partly supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Center “Automatic Verification and Analysis of Complex Systems” (SFB/TR 14 AVACS1), by the European Research Council (ERC) under grant 267989 (QUAREM) and by the Austrian Science Fund (FWF) under grants S11402-N23 (RiSE) and Z211-N23 (Wittgenstein Award). J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734.","date_published":"2016-11-01T00:00:00Z","project":[{"_id":"25EE3708-B435-11E9-9278-68D0E5697425","grant_number":"267989","name":"Quantitative Reactive Modeling","call_identifier":"FP7"},{"call_identifier":"FWF","name":"Rigorous Systems Engineering","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FWF","name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7"}],"status":"public","publication":"Biosystems","date_updated":"2023-02-23T10:08:46Z","_id":"1148","type":"journal_article","doi":"10.1016/j.biosystems.2016.07.005","publisher":"Elsevier","quality_controlled":"1","page":"15 - 25"},{"quality_controlled":"1","ddc":["000","570"],"_id":"10794","date_updated":"2022-02-25T11:59:23Z","type":"journal_article","article_processing_charge":"No","doi":"10.3389/fenvs.2015.00042","publisher":"Frontiers","ec_funded":1,"acknowledgement":"The authors would like to acknowledge contributions from Baptiste Mottet who performed preliminary analysis regarding parameter inference for the considered case study in a student project (Mottet, 2014/2015).\r\nThe research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement No. [291734] and from SystemsX under the project SignalX.","date_published":"2015-06-10T00:00:00Z","status":"public","publication":"Frontiers in Environmental Science","project":[{"call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"keyword":["General Environmental Science"],"year":"2015","file_date_updated":"2022-02-25T11:55:26Z","publication_identifier":{"issn":["2296-665X"]},"publication_status":"published","has_accepted_license":"1","intvolume":"         3","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)"},"abstract":[{"lang":"eng","text":"Mathematical models are of fundamental importance in the understanding of complex population dynamics. For instance, they can be used to predict the population evolution starting from different initial conditions or to test how a system responds to external perturbations. For this analysis to be meaningful in real applications, however, it is of paramount importance to choose an appropriate model structure and to infer the model parameters from measured data. While many parameter inference methods are available for models based on deterministic ordinary differential equations, the same does not hold for more detailed individual-based models. Here we consider, in particular, stochastic models in which the time evolution of the species abundances is described by a continuous-time Markov chain. These models are governed by a master equation that is typically difficult to solve. Consequently, traditional inference methods that rely on iterative evaluation of parameter likelihoods are computationally intractable. The aim of this paper is to present recent advances in parameter inference for continuous-time Markov chain models, based on a moment closure approximation of the parameter likelihood, and to investigate how these results can help in understanding, and ultimately controlling, complex systems in ecology. Specifically, we illustrate through an agricultural pest case study how parameters of a stochastic individual-based model can be identified from measured data and how the resulting model can be used to solve an optimal control problem in a stochastic setting. In particular, we show how the matter of determining the optimal combination of two different pest control methods can be formulated as a chance constrained optimization problem where the control action is modeled as a state reset, leading to a hybrid system formulation."}],"volume":3,"date_created":"2022-02-25T11:42:25Z","article_type":"original","day":"10","scopus_import":"1","author":[{"first_name":"Francesca","last_name":"Parise","full_name":"Parise, Francesca"},{"last_name":"Lygeros","full_name":"Lygeros, John","first_name":"John"},{"last_name":"Ruess","full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","orcid":"0000-0003-1615-3282"}],"title":"Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study","oa_version":"Published Version","citation":{"short":"F. Parise, J. Lygeros, J. Ruess, Frontiers in Environmental Science 3 (2015).","ieee":"F. Parise, J. Lygeros, and J. Ruess, “Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study,” <i>Frontiers in Environmental Science</i>, vol. 3. Frontiers, 2015.","ama":"Parise F, Lygeros J, Ruess J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. <i>Frontiers in Environmental Science</i>. 2015;3. doi:<a href=\"https://doi.org/10.3389/fenvs.2015.00042\">10.3389/fenvs.2015.00042</a>","mla":"Parise, Francesca, et al. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” <i>Frontiers in Environmental Science</i>, vol. 3, 42, Frontiers, 2015, doi:<a href=\"https://doi.org/10.3389/fenvs.2015.00042\">10.3389/fenvs.2015.00042</a>.","apa":"Parise, F., Lygeros, J., &#38; Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. <i>Frontiers in Environmental Science</i>. Frontiers. <a href=\"https://doi.org/10.3389/fenvs.2015.00042\">https://doi.org/10.3389/fenvs.2015.00042</a>","chicago":"Parise, Francesca, John Lygeros, and Jakob Ruess. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” <i>Frontiers in Environmental Science</i>. Frontiers, 2015. <a href=\"https://doi.org/10.3389/fenvs.2015.00042\">https://doi.org/10.3389/fenvs.2015.00042</a>.","ista":"Parise F, Lygeros J, Ruess J. 2015. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. 3, 42."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"language":[{"iso":"eng"}],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"file":[{"checksum":"26c222487564e1be02a11d688d6f769d","relation":"main_file","content_type":"application/pdf","access_level":"open_access","file_name":"2015_FrontiersEnvironmScience_Parise.pdf","success":1,"file_id":"10795","date_updated":"2022-02-25T11:55:26Z","creator":"dernst","date_created":"2022-02-25T11:55:26Z","file_size":1371201}],"article_number":"42","month":"06"},{"publist_id":"5492","related_material":{"record":[{"id":"1148","relation":"later_version","status":"public"}]},"year":"2015","conference":{"location":"Nantes, France","name":"CMSB: Computational Methods in Systems Biology","end_date":"2015-09-18","start_date":"2015-09-16"},"date_published":"2015-09-01T00:00:00Z","ec_funded":1,"project":[{"call_identifier":"FP7","grant_number":"267989","name":"Quantitative Reactive Modeling","_id":"25EE3708-B435-11E9-9278-68D0E5697425"},{"name":"The Wittgenstein Prize","grant_number":"Z211","call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"name":"Rigorous Systems Engineering","grant_number":"S 11407_N23","call_identifier":"FWF","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7"}],"status":"public","type":"conference","series_title":"Lecture Notes in Computer Science","date_updated":"2023-02-21T16:17:24Z","_id":"1658","publisher":"Springer","doi":"10.1007/978-3-319-23401-4_8","alternative_title":["LNCS"],"quality_controlled":"1","page":"77 - 89","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"month":"09","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Bogomolov, Sergiy, et al. <i>Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks</i>. Vol. 9308, Springer, 2015, pp. 77–89, doi:<a href=\"https://doi.org/10.1007/978-3-319-23401-4_8\">10.1007/978-3-319-23401-4_8</a>.","apa":"Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., &#38; Schilling, C. (2015). Adaptive moment closure for parameter inference of biochemical reaction networks. Presented at the CMSB: Computational Methods in Systems Biology, Nantes, France: Springer. <a href=\"https://doi.org/10.1007/978-3-319-23401-4_8\">https://doi.org/10.1007/978-3-319-23401-4_8</a>","ista":"Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. 2015. Adaptive moment closure for parameter inference of biochemical reaction networks. 9308, 77–89.","chicago":"Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. <a href=\"https://doi.org/10.1007/978-3-319-23401-4_8\">https://doi.org/10.1007/978-3-319-23401-4_8</a>.","short":"S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89.","ieee":"S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive moment closure for parameter inference of biochemical reaction networks,” vol. 9308. Springer, pp. 77–89, 2015.","ama":"Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. Adaptive moment closure for parameter inference of biochemical reaction networks. 2015;9308:77-89. doi:<a href=\"https://doi.org/10.1007/978-3-319-23401-4_8\">10.1007/978-3-319-23401-4_8</a>"},"language":[{"iso":"eng"}],"date_created":"2018-12-11T11:53:18Z","volume":9308,"title":"Adaptive moment closure for parameter inference of biochemical reaction networks","oa_version":"None","author":[{"orcid":"0000-0002-0686-0365","first_name":"Sergiy","last_name":"Bogomolov","id":"369D9A44-F248-11E8-B48F-1D18A9856A87","full_name":"Bogomolov, Sergiy"},{"full_name":"Henzinger, Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","first_name":"Thomas A","orcid":"0000−0002−2985−7724"},{"last_name":"Podelski","full_name":"Podelski, Andreas","first_name":"Andreas"},{"last_name":"Ruess","full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","orcid":"0000-0003-1615-3282"},{"first_name":"Christian","last_name":"Schilling","full_name":"Schilling, Christian"}],"day":"01","scopus_import":1,"publication_status":"published","abstract":[{"lang":"eng","text":"Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space."}],"intvolume":"      9308"},{"year":"2015","month":"02","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publist_id":"5238","article_number":"8","language":[{"iso":"eng"}],"publication":"ACM Transactions on Modeling and Computer Simulation","status":"public","issue":"2","citation":{"ista":"Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8.","chicago":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM, 2015. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>.","apa":"Ruess, J., &#38; Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>","mla":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2, 8, ACM, 2015, doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>.","ama":"Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. 2015;25(2). doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>","ieee":"J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2. ACM, 2015.","short":"J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015)."},"acknowledgement":"HYCON2; EC; European Commission\r\n","date_published":"2015-02-01T00:00:00Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.1145/2688906","author":[{"full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","orcid":"0000-0003-1615-3282","first_name":"Jakob"},{"first_name":"John","last_name":"Lygeros","full_name":"Lygeros, John"}],"day":"01","scopus_import":1,"publisher":"ACM","oa_version":"None","title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","date_updated":"2021-01-12T06:53:41Z","volume":25,"_id":"1861","type":"journal_article","date_created":"2018-12-11T11:54:25Z","intvolume":"        25","abstract":[{"text":"Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.","lang":"eng"}],"quality_controlled":"1","publication_status":"published"},{"ec_funded":1,"pmid":1,"date_published":"2015-06-30T00:00:00Z","acknowledgement":"J.R., F.P., and J.L. acknowledge support from the European Commission under the Network of Excellence HYCON2 (highly-complex and networked control systems) and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). ","publication":"PNAS","status":"public","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"publist_id":"5633","year":"2015","external_id":{"pmid":["26085136"]},"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/","open_access":"1"}],"quality_controlled":"1","page":"8148 - 8153","_id":"1538","date_updated":"2021-01-12T06:51:27Z","type":"journal_article","doi":"10.1073/pnas.1423947112","publisher":"National Academy of Sciences","citation":{"ama":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. <i>PNAS</i>. 2015;112(26):8148-8153. doi:<a href=\"https://doi.org/10.1073/pnas.1423947112\">10.1073/pnas.1423947112</a>","ieee":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative experiment design guides the characterization of a light-inducible gene expression circuit,” <i>PNAS</i>, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153, 2015.","short":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153.","chicago":"Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash, and John Lygeros. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” <i>PNAS</i>. National Academy of Sciences, 2015. <a href=\"https://doi.org/10.1073/pnas.1423947112\">https://doi.org/10.1073/pnas.1423947112</a>.","ista":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. 2015. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 112(26), 8148–8153.","apa":"Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., &#38; Lygeros, J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1423947112\">https://doi.org/10.1073/pnas.1423947112</a>","mla":"Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” <i>PNAS</i>, vol. 112, no. 26, National Academy of Sciences, 2015, pp. 8148–53, doi:<a href=\"https://doi.org/10.1073/pnas.1423947112\">10.1073/pnas.1423947112</a>."},"issue":"26","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"language":[{"iso":"eng"}],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"month":"06","publication_status":"published","intvolume":"       112","abstract":[{"lang":"eng","text":"Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time."}],"volume":112,"date_created":"2018-12-11T11:52:36Z","day":"30","scopus_import":1,"author":[{"last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","first_name":"Jakob"},{"first_name":"Francesca","last_name":"Parise","full_name":"Parise, Francesca"},{"first_name":"Andreas","full_name":"Milias Argeitis, Andreas","last_name":"Milias Argeitis"},{"last_name":"Khammash","full_name":"Khammash, Mustafa","first_name":"Mustafa"},{"first_name":"John","last_name":"Lygeros","full_name":"Lygeros, John"}],"title":"Iterative experiment design guides the characterization of a light-inducible gene expression circuit","oa_version":"Submitted Version"},{"oa":1,"language":[{"iso":"eng"}],"pubrep_id":"593","citation":{"ama":"Ruess J. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. <i>Journal of Chemical Physics</i>. 2015;143(24). doi:<a href=\"https://doi.org/10.1063/1.4937937\">10.1063/1.4937937</a>","ieee":"J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space,” <i>Journal of Chemical Physics</i>, vol. 143, no. 24. American Institute of Physics, 2015.","short":"J. Ruess, Journal of Chemical Physics 143 (2015).","ista":"Ruess J. 2015. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 143(24), 244103.","chicago":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” <i>Journal of Chemical Physics</i>. American Institute of Physics, 2015. <a href=\"https://doi.org/10.1063/1.4937937\">https://doi.org/10.1063/1.4937937</a>.","apa":"Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. <i>Journal of Chemical Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1063/1.4937937\">https://doi.org/10.1063/1.4937937</a>","mla":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” <i>Journal of Chemical Physics</i>, vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:<a href=\"https://doi.org/10.1063/1.4937937\">10.1063/1.4937937</a>."},"issue":"24","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"12","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"article_number":"244103","file":[{"file_id":"4641","date_updated":"2020-07-14T12:45:01Z","creator":"system","file_size":605355,"date_created":"2018-12-12T10:07:43Z","checksum":"838657118ae286463a2b7737319f35ce","relation":"main_file","content_type":"application/pdf","access_level":"open_access","file_name":"IST-2016-593-v1+1_Minimal_moment_equations.pdf"}],"has_accepted_license":"1","abstract":[{"text":"Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space. ","lang":"eng"}],"intvolume":"       143","file_date_updated":"2020-07-14T12:45:01Z","publication_status":"published","scopus_import":1,"day":"22","author":[{"last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob","first_name":"Jakob","orcid":"0000-0003-1615-3282"}],"oa_version":"Published Version","title":"Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space","volume":143,"date_created":"2018-12-11T11:52:36Z","publication":"Journal of Chemical Physics","status":"public","project":[{"call_identifier":"FP7","name":"Quantitative Reactive Modeling","grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425"},{"grant_number":"S 11407_N23","name":"Rigorous Systems Engineering","call_identifier":"FWF","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FWF","name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"ec_funded":1,"date_published":"2015-12-22T00:00:00Z","year":"2015","publist_id":"5632","ddc":["000"],"quality_controlled":"1","doi":"10.1063/1.4937937","publisher":"American Institute of Physics","_id":"1539","date_updated":"2021-01-12T06:51:28Z","type":"journal_article"}]