{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:40:02Z","timestamp":1755891602121,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,6,16]]},"DOI":"10.1145\/3608251.3608277","type":"proceedings-article","created":{"date-parts":[[2023,8,17]],"date-time":"2023-08-17T12:15:01Z","timestamp":1692274501000},"page":"22-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Computer Simulation of Nonlinear Mixed-Effect Models with Ordinary Differential Equations for Genetic Regulation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3375-4426","authenticated-orcid":false,"given":"Aimin","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Henan University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0797-0531","authenticated-orcid":false,"given":"Tianshou","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Sun Yat-sen University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6191-0209","authenticated-orcid":false,"given":"Tianhai","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Mathematics, Monash University, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,17]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar3131"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","unstructured":"Jun Ding Nadav Sharon and Ziv Bar-Joseph. 2022. Temporal modelling using single-cell transcriptomics.\u00a0Nature Reviews Genetics\u00a023(6): 355-368. https:\/\/doi.org\/10.1038\/s41576-021-00444-7","DOI":"10.1038\/s41576-021-00444-7"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-021-00341-z"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","unstructured":"David L\u00e4hnemann Johannes K\u00f6ster Ewa Szczurek Davis J. McCarthy Stephanie C. Hicks Mark D. Robinson Catalina A. Vallejos 2020. Eleven grand challenges in single-cell data science.\u00a0Genome biology\u00a021(1): 1-35. https:\/\/doi.org\/10.1186\/s13059-020-1926-6.","DOI":"10.1186\/s13059-020-1926-6"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","unstructured":"Aimin Chen Tianshou Zhou and Tianhai Tian. 2022. Integrated Pipelines for Inferring Gene Regulatory Networks from Single-Cell Data.\u00a0Current Bioinformatics\u00a017(7): 559-564. https:\/\/doi.org\/10.2174\/1574893617666220511234247","DOI":"10.2174\/1574893617666220511234247"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.2307\/2532087"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1198\/1085711032697"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","unstructured":"Se Yoon Lee. 2022. Bayesian Nonlinear Models for Repeated Measurement Data: An Overview Implementation and Applications.\"\u00a0Mathematics\u00a010(6): 898. https:\/\/doi.org\/10.3390\/math10060898","DOI":"10.3390\/math10060898"},{"key":"e_1_3_2_1_9_1","volume-title":"Boik","author":"Boik John C","year":"2008","unstructured":"John C Boik, Robert A. Newman, and Robert J. Boik. 2008. Quantifying synergism\/antagonism using nonlinear mixed\u2010effects modeling: A simulation study.\u00a0Statistics in medicine. 27(7): 1040-1061."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/icesjms\/fsu213"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","unstructured":"Martin Berglund Mikael Sunn\u00e5ker Martin Adiels Mats Jirstrand and Bernt Wennberg. 2012. Investigations of a compartmental model for leucine kinetics using non-linear mixed effects models with ordinary and stochastic differential equations.\u00a0Mathematical medicine and biology: a journal of the IMA.\u00a029(4): 361-384. https:\/\/doi.org\/10.1093\/imammb\/dqr021","DOI":"10.1093\/imammb\/dqr021"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","unstructured":"Reza Drikvandi. 2017. Nonlinear mixed-effects models for pharmacokinetic data analysis: assessment of the random-effects distribution.\u00a0Journal of pharmacokinetics and pharmacodynamics\u00a044(3): 223-232. https:\/\/doi.org\/10.1007\/s10928-017-9510-8","DOI":"10.1007\/s10928-017-9510-8"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Robert J Bauer. 2019. NONMEM tutorial part I: description of commands and options with simple examples of population analysis.\u00a0CPT: pharmacometrics & systems pharmacology\u00a08(8): 525-537.","DOI":"10.1002\/psp4.12404"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12918-015-0203-x"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","unstructured":"Sebastian Persson Niek Welkenhuysen Sviatlana Shashkova Samuel Wiqvist Patrick Reith Gregor W. Schmidt Umberto Picchini and Marija Cvijovic. 2022. Scalable and flexible inference framework for stochastic dynamic single-cell models.\u00a0PLoS Computational Biology\u00a018(5): e1010082. https:\/\/doi.org\/10.1371\/journal.pcbi.1010082","DOI":"10.1371\/journal.pcbi.1010082"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","unstructured":"Estelle Kuhn and Marc Lavielle. 2005. Maximum likelihood estimation in nonlinear mixed effects models.\u00a0Computational statistics & data analysis\u00a049(4): 1020-1038. https:\/\/doi.org\/10.1016\/j.csda.2004.07.002.","DOI":"10.1016\/j.csda.2004.07.002"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/02664763.2022.2034141"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2013.02.011"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","unstructured":"Dipak K Dey Ming-Hui Chen and Hong Chang. 1997. Bayesian approach for nonlinear random effects models.\"\u00a0Biometrics: . \u00a053(4): 1239-1252. https:\/\/doi.org\/10.2307\/2533493","DOI":"10.2307\/2533493"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","unstructured":"Umberto Picchini. 2014. Inference for SDE models via approximate Bayesian computation.\u00a0Journal of Computational and Graphical Statistics.\u00a023(4): 1080-1100. https:\/\/doi.org\/10.1080\/10618600.2013.866048","DOI":"10.1080\/10618600.2013.866048"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2022.136319"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1198\/106186006X96854"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610918.2022.2066696"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42519-021-00172-5"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","unstructured":"Weng Lee Hongyue Dai Yihui Zhan Yudong He Sergey B. Stepaniants and Douglas E. Bassett. \"Rosetta error model for gene expression analysis.\"\u00a0Bioinformatics\u00a022 no. 9 (2006): 1111-1121. https:\/\/doi.org\/10.1093\/bioinformatics\/btl045","DOI":"10.1093\/bioinformatics\/btl045"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","unstructured":"Richard R Stein Debora S. Marks and Chris Sander. 2015. Inferring pairwise interactions from biological data using maximum-entropy probability models.\"\u00a0PLoS computational biology.\u00a011(7): e1004182. https:\/\/doi.org\/10.1371\/journal.pcbi.1004182","DOI":"10.1371\/journal.pcbi.1004182"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","unstructured":"Zhimin Deng Xinan Zhang and Tianhai Tian. 2018. Inference of model parameters using particle filter algorithm and Copula distributions.\u00a0IEEE\/ACM transactions on computational biology and bioinformatics\u00a017(4): 1231-1240. https:\/\/doi.org\/10.1109\/TCBB.2018.2880974","DOI":"10.1109\/TCBB.2018.2880974"}],"event":{"name":"ICCMS 2023: 2023 The 15th International Conference on Computer Modeling and Simulation","acronym":"ICCMS 2023","location":"Dalian China"},"container-title":["2023 The 15th International Conference on Computer Modeling and Simulation"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3608251.3608277","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3608251.3608277","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T19:04:20Z","timestamp":1755889460000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3608251.3608277"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,16]]},"references-count":27,"alternative-id":["10.1145\/3608251.3608277","10.1145\/3608251"],"URL":"https:\/\/doi.org\/10.1145\/3608251.3608277","relation":{},"subject":[],"published":{"date-parts":[[2023,6,16]]},"assertion":[{"value":"2023-08-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}