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The differentiation to cardiomyocytes replicates the embryonic heart development, potentially supporting cardiac regeneration. Cardiomyogenesis is controlled by complex post-transcriptional regulation that affects the construction of gene regulatory networks (GRNs), such as: alternative polyadenylation (APA), length changes in untranslated regulatory regions (3\u2032UTRs), and microRNA (miRNA) regulation. To deepen our understanding of the cardiomyogenesis process, we have modeled a GRN for each day of cardiomyocyte differentiation. Then, each GRN was automatically transformed by four transformation rules to a Petri net and simulated using the software VANESA. The Petri nets highlighted the relationship between genes and alternative isoforms, emphasizing the inhibition of miRNA on APA isoforms with varying 3\u2032UTR lengths. Moreover, <jats:italic>in silico<\/jats:italic> simulation of miRNA knockout enabled the visualization of the consequential effects on isoform expression. Our Petri net models provide a resourceful tool and holistic perspective to investigate the functional orchestra of transcript regulation that differentiate hESCs to cardiomyocytes. Additionally, the models can be adapted to investigate post-transcriptional GRN in other biological contexts.<\/jats:p>","DOI":"10.1515\/jib-2024-0037","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T03:56:01Z","timestamp":1750391761000},"source":"Crossref","is-referenced-by-count":0,"title":["Petri net modeling and simulation of post-transcriptional regulatory networks of human embryonic stem cell (hESC) differentiation to cardiomyocytes"],"prefix":"10.1515","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1133-7464","authenticated-orcid":false,"given":"Aruana F. F.","family":"Hansel-Fr\u00f6se","sequence":"first","affiliation":[{"name":"Laboratory of Basic Stem Cell Biology, Carlos Chagas Institute , Oswaldo Cruz Foundation (FIOCRUZ\/PR) , Curitiba , Brazil"},{"name":"Division of Immunobiology, Institute of Immunology, Center for Pathophysiology, Infectiology and Immunology , Medical University of Vienna , Vienna , Austria"}]},{"given":"Christoph","family":"Brinkrolf","sequence":"additional","affiliation":[{"name":"Faculty of Technology, Bioinformatics\/Medical Informatics Department , Bielefeld University , Bielefeld , Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9846-7212","authenticated-orcid":false,"given":"Marcel","family":"Friedrichs","sequence":"additional","affiliation":[{"name":"Faculty of Technology, Bioinformatics\/Medical Informatics Department , Bielefeld University , Bielefeld , Germany"}]},{"given":"Bruno","family":"Dallagiovanna","sequence":"additional","affiliation":[{"name":"Laboratory of Basic Stem Cell Biology, Carlos Chagas Institute , Oswaldo Cruz Foundation (FIOCRUZ\/PR) , Curitiba , Brazil"}]},{"given":"Lucia","family":"Spangenberg","sequence":"additional","affiliation":[{"name":"Bioinformatics Unit , Pasteur Institute of Montevideo , Montevideo , Uruguay"},{"name":"Departamento Basico de Medicina , Hospital de Clinicas, Universidad de la Rep\u00fablica (Udelar) , Montevideo , Uruguay"}]}],"member":"374","published-online":{"date-parts":[[2025,6,23]]},"reference":[{"key":"2025080610581964190_j_jib-2024-0037_ref_001","unstructured":"World Health Organization. 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Cell Stem Cell 2015;17:11\u201322. https:\/\/doi.org\/10.1016\/j.stem.2015.06.007.","DOI":"10.1016\/j.stem.2015.06.007"},{"key":"2025080610581964190_j_jib-2024-0037_ref_004","doi-asserted-by":"crossref","unstructured":"Rikhtegar, R, Pezeshkian, M, Dolati, S, Safaie, N, Rad, AA, Mahdipour, M, et al.. Stem cells as therapy for heart disease: iPSCs, ESCs, CSCs, and skeletal myoblasts. Biomed Pharmacother 2019;109:304\u201313. https:\/\/doi.org\/10.1016\/j.biopha.2018.10.065.","DOI":"10.1016\/j.biopha.2018.10.065"},{"key":"2025080610581964190_j_jib-2024-0037_ref_005","doi-asserted-by":"crossref","unstructured":"Zhang, Y, Mignone, J, Maclellan, WR. Cardiac regeneration and stem cells. Physiol Rev 2015;95:1189\u2013204. https:\/\/doi.org\/10.1152\/physrev.00021.2014.","DOI":"10.1152\/physrev.00021.2014"},{"key":"2025080610581964190_j_jib-2024-0037_ref_006","doi-asserted-by":"crossref","unstructured":"Fuchs, E, Segre, JA. Stem cells: a new lease on life. 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J Integr Bioinform 2014;11:239. https:\/\/doi.org\/10.2390\/biecoll-jib-2014-239.","DOI":"10.1515\/jib-2014-239"},{"key":"2025080610581964190_j_jib-2024-0037_ref_035","doi-asserted-by":"crossref","unstructured":"Kanehisa, M, Furumichi, M, Tanabe, M, Sato, Y, Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 2017;1:D353\u201361. https:\/\/doi.org\/10.1093\/nar\/gkw1092.","DOI":"10.1093\/nar\/gkw1092"},{"key":"2025080610581964190_j_jib-2024-0037_ref_036","doi-asserted-by":"crossref","unstructured":"Heiner, M, Sriram, K. Structural analysis to determine the core of hypoxia response network. PLoS One 2010;1:5.","DOI":"10.1371\/journal.pone.0008600"},{"key":"2025080610581964190_j_jib-2024-0037_ref_037","doi-asserted-by":"crossref","unstructured":"Minervini, G, Panizzoni, E, Giollo, M, Masiero, A, Ferrari, C, Tosatto, SCE. Design and analysis of a Petri net model of the Von Hippel-Lindau (VHL) tumor suppressor interaction network. 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Front Immunol 2018;3:9. https:\/\/doi.org\/10.3389\/fimmu.2018.00393.","DOI":"10.3389\/fimmu.2018.00393"},{"key":"2025080610581964190_j_jib-2024-0037_ref_042","doi-asserted-by":"crossref","unstructured":"Azim, N, Ahmad, J, Iqbal, N, Siddiqa, A, Majid, A, Ashraf, J, et al.. Petri net modelling approach for analysing the behaviour of Wnt\/\u03b2-catenin and Wnt\/Ca2+ signalling pathways in arrhythmogenic right ventricular cardiomyopathy. IET Syst Biol 2020;14:350\u201367. https:\/\/doi.org\/10.1049\/iet-syb.2020.0038.","DOI":"10.1049\/iet-syb.2020.0038"},{"key":"2025080610581964190_j_jib-2024-0037_ref_043","doi-asserted-by":"crossref","unstructured":"Bonzanni, N, Feenstra, KA, Fokkink, W, Heringa, J. Petri nets are a biologist\u2019s best friend. In: Fages, F, Piazza, C, editors. Formal methods in macro-biology. Cham: Springer International Publishing; 2014:102\u201316 pp.","DOI":"10.1007\/978-3-319-10398-3_8"},{"key":"2025080610581964190_j_jib-2024-0037_ref_044","doi-asserted-by":"crossref","unstructured":"Troncale, S, Tahi, F, Campard, D, Vannier, JP, Guespin, J. Modeling and simulation with hybrid functional petri nets of the role of interleukin-6 in human early haematopoiesis. Pac Symp Biocomput 2006:427\u201338.","DOI":"10.1142\/9789812701626_0039"},{"key":"2025080610581964190_j_jib-2024-0037_ref_045","doi-asserted-by":"crossref","unstructured":"Li, J, Pandey, V, Kessler, T, Lehrach, H, Wierling, C. Modeling of miRNA and drug action in the EGFR signaling pathway. PLoS One 2012;1:7.","DOI":"10.1371\/journal.pone.0030140"},{"key":"2025080610581964190_j_jib-2024-0037_ref_046","doi-asserted-by":"crossref","unstructured":"Li, J, Mansmann, UR. Modeling of non-steroidal anti-inflammatory drug effect within signaling pathways and miRNA-regulation pathways. 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Clin Sci 2019;133:1387\u201399. https:\/\/doi.org\/10.1042\/cs20190099.","DOI":"10.1042\/CS20190099"},{"key":"2025080610581964190_j_jib-2024-0037_ref_074","doi-asserted-by":"crossref","unstructured":"Yang, H, Song, S, Li, J, Li, Y, Feng, J, Sun, Q, et al.. Omentin-1 drives cardiomyocyte cell cycle arrest and metabolic maturation by interacting with BMP7. Cell Mol Life Sci 2023;7:80.","DOI":"10.1007\/s00018-023-04829-1"},{"key":"2025080610581964190_j_jib-2024-0037_ref_075","doi-asserted-by":"crossref","unstructured":"Parikh, A, Wu, J, Blanton, RM, Tzanakakis, ES. Signaling pathways and gene regulatory networks in cardiomyocyte differentiation. Tissue Eng B Rev 2015;21:377\u201392. https:\/\/doi.org\/10.1089\/ten.teb.2014.0662.","DOI":"10.1089\/ten.teb.2014.0662"},{"key":"2025080610581964190_j_jib-2024-0037_ref_076","doi-asserted-by":"crossref","unstructured":"Fu, W, Liao, Q, Li, L, Shi, Y, Zeng, A, Zeng, C, et al.. 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