{"doi":"10.1007/s00285-022-01739-x","title":"Identifiability analysis for models of the translation kinetics after mRNA transfection","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Mechanistic models are a powerful tool to gain insights into biological processes. The parameters of such models, e.g. kinetic rate constants, usually cannot be measured directly but need to be inferred from experimental data. In this article, we study dynamical models of the translation kinetics after mRNA transfection and analyze their parameter identifiability. That is, whether parameters can be uniquely determined from perfect or realistic data in theory and practice. Previous studies have considered ordinary differential equation (ODE) models of the process, and here we formulate a stochastic differential equation (SDE) model. For both model types, we consider structural identifiability based on the model equations and practical identifiability based on simulated as well as experimental data and find that the SDE model provides better parameter identifiability than the ODE model. Moreover, our analysis shows that even for those parameters of the ODE model that are considered to be identifiable, the obtained estimates are sometimes unreliable. Overall, our study clearly demonstrates the relevance of considering different modeling approaches and that stochastic models can provide more reliable and informative results.</jats:p>","journal":"Journal of Mathematical Biology","year":2022,"id":21907,"datarank":0.32561127571301174,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.056847355328803494,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.056847355328803494,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":5,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":137640,"name":"Jan Hasenauer","orcid":"0000-0002-4935-3312","position":1,"is_corresponding":false},{"id":4422,"name":"Christiane Fuchs","orcid":"0000-0003-3565-8315","position":2,"is_corresponding":false},{"id":137639,"name":"Susanne Pieschner","orcid":"0000-0002-2916-7782","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":1.6094379124341003,"endowment":1.6094379124341003,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"35577967","pmcid":"PMC9110294","openalex_id":"https://openalex.org/W4280491311","authors":[],"funders":[{"funder_name":"Bundesministerium für Bildung und Forschung","grant_id":"01DH17024","title":null},{"funder_name":"Bundesministerium für Bildung und Forschung","grant_id":"01KI20271","title":null},{"funder_name":"European Commission","grant_id":"10101616","title":null},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"HA 7376/3-1","title":null},{"funder_name":"Volkswagen Foundation","grant_id":"99 450","title":null},{"funder_name":"Helmholtz-Gemeinschaft","grant_id":"pilot project “Uncertainty Quantification”","title":null},{"funder_name":"Deutsche Forschungsgemeinschaft","grant_id":"unidentified","title":"unidentified"},{"funder_name":"European Commission","grant_id":"101016167","title":"Connecting European Cohorts to Increase Common and Effective Response to SARS-CoV-2 Pandemic: ORCHESTRA"}],"total_grants":8,"fwci":0.2599,"citation_percentile":0.51088284,"influential_citations":0,"citation_trend":[{"year":2022,"count":1},{"year":2024,"count":1},{"year":2025,"count":1},{"year":2026,"count":1}],"oa_status":"hybrid","license":"cc-by","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/s00285-022-01739-x.pdf","host_type":"journal"},{"url":"https://link.springer.com/content/pdf/10.1007/s00285-022-01739-x.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1007/s00285-022-01739-x/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1007/s00285-022-01739-x","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/35577967","host_type":"repository"},{"url":"https://push-zb.helmholtz-munich.de/frontdoor.php?source_opus=64987","host_type":"repository"},{"url":"https://mediatum.ub.tum.de/1761548","host_type":"repository"},{"url":"https://mediatum.ub.tum.de/1792983","host_type":"repository"},{"url":"https://push-zb.helmholtz-muenchen.de/frontdoor.php?source_opus=64987","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9110294","host_type":"repository"},{"url":"https://push-zb.helmholtz-munich.de/deliver.php?id=32905","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC9110294","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC9110294?pdf=render","host_type":"Europe_PMC"},{"url":"https://doi.org/10.1101/2021.05.18.444633","host_type":""},{"url":"https://www.biorxiv.org/content/biorxiv/early/2021/05/18/2021.05.18.444633.full.pdf","host_type":""},{"url":"http://dx.doi.org/10.1007/s00285-022-01739-x","host_type":""},{"url":"https://zbmath.org/7529648","host_type":""},{"url":"https://dx.doi.org/10.1101/2021.05.18.444633","host_type":""},{"url":"https://pub.uni-bielefeld.de/record/2963169","host_type":""},{"url":"https://mediatum.ub.tum.de/doc/1761548/document.pdf","host_type":""},{"url":"http://dx.doi.org/10.1101/2021.05.18.444633","host_type":""}],"fields_of_study":["Gene Regulatory Network Analysis","RNA and protein synthesis mechanisms","RNA Research and Splicing","0301 basic medicine","0303 health sciences","ddc:","03 medical and health sciences","Kinetics","Models, Biological","RNA, Messenger","Transfection"],"mesh_terms":["Kinetics","Models, Biological","RNA, Messenger","Transfection"],"keywords":["Identifiability","Ode","Ordinary differential equation","Applied mathematics","Mathematics","Stochastic differential equation","Estimation theory","Statistical physics","Biological system","Translation (biology)","Differential equation","Computer science","Statistics","Mathematical analysis","Physics","Messenger RNA","Biology","Parameter Identifiability","Stochastic Modeling","Chemical Langevin Equation","Mrna Transfection","Differential Equation Models","Itô Diffusion Process","Article ; Differential equation models ; Stochastic modeling ; Itô diffusion process ; Chemical Langevin equation ; Parameter identifiability ; mRNA transfection ; 92-08 ; 60J70 ; 62F15 ; Mathematical Sciences","Applications of stochastic analysis (to PDEs, etc.)","Transfection","Models, Biological","Article","Kinetics","Qualitative investigation and simulation of ordinary differential equation models","RNA, Messenger","Chemical Langevin Equation ; Differential Equation Models ; Itô Diffusion Process ; Parameter Identifiability ; Stochastic Modeling ; Mrna Transfection","Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.)"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-06T17:12:58.099587Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}