{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T04:12:33Z","timestamp":1748405553573,"version":"3.41.0"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-20-CE17-0007","ANR-20-CE17-0007"],"award-info":[{"award-number":["ANR-20-CE17-0007","ANR-20-CE17-0007"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Syst Biol Appl"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The advent of single-cell transcriptomics (scRNA-seq) has provided unprecedented access to specific cell type signatures, including during transient developmental stages. One key expectation is to be able to model gene regulatory networks (GRNs) from the cell-type scRNA-seq signatures. However, most computed GRNs are static models and lack the ability to predict the effects of genetic or environmental perturbations. Here, we focus on the maturation process of the trophectoderm (TE), the outer layer of cells of human embryos, which is critical for their ability to attach to the endometrium. Addressing this challenge required overcoming two major limitations: <jats:italic>(i)<\/jats:italic> handling the search space generated by the high dimensionality of single-cell data, <jats:italic>(ii)<\/jats:italic> the lack of feasible perturbation data for certain biological systems, which limits validation or generation of dynamic models. To address these challenges, we created SCIBORG, a computational package designed to infer Boolean networks (BNs) of gene regulation by integrating single-cell transcriptomic data with prior knowledge networks. SCIBORG uses logic programming to manage the combinatorial explosion. It learns two distinct BN families for each of the two developmental stages studied (TE and mature TE) by identifying specific gene regulatory mechanisms. The comparison between these two BN families reveals different pathways, identifying potential key genes critical for trophectoderm maturation. In silico validation through cell classification into studied stages reveals balanced precision 67% - 73% for inferred BN families. We demonstrate that SCIBORG is a powerful tool that integrates the diversity between gene expression profiles of cells at two different stages of development in the construction of Boolean models.<\/jats:p>","DOI":"10.1038\/s41540-025-00537-7","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T11:50:59Z","timestamp":1748346659000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep exploration of logical models of cell differentiation in human preimplantation embryos"],"prefix":"10.1038","volume":"11","author":[{"given":"Mathieu","family":"Bolteau","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C\u00e9lia","family":"Messaoudi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"David","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00e9r\u00e9mie","family":"Bourdon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carito","family":"Guziolowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"537_CR1","doi-asserted-by":"publisher","first-page":"1586","DOI":"10.1093\/humrep\/dey242","volume":"33","author":"C De Geyter","year":"2018","unstructured":"De Geyter, C. et al. ART in Europe, 2014: results generated from European registries by ESHRE\u2020: The European IVF-monitoring Consortium (EIM)\u2021 for the European Society of Human Reproduction and Embryology (ESHRE). Hum. Reprod. 33, 1586\u20131601 (2018).","journal-title":"Hum. Reprod."},{"key":"537_CR2","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1038\/nmeth.4463","volume":"14","author":"S Aibar","year":"2017","unstructured":"Aibar, S. et al. Scenic: single-cell regulatory network inference and clustering. Nat. methods 14, 1083\u20131086 (2017).","journal-title":"Nat. methods"},{"key":"537_CR3","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1038\/s44320-025-00088-3","volume":"21","author":"M Stock","year":"2025","unstructured":"Stock, M. et al. Leveraging prior knowledge to infer gene regulatory networks from single-cell rna-sequencing data. Mol. Syst. Biol. 21, 214\u2013230 (2025).","journal-title":"Mol. Syst. Biol."},{"key":"537_CR4","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1038\/nbt.3154","volume":"33","author":"V Moignard","year":"2015","unstructured":"Moignard, V. et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269\u2013276 (2015).","journal-title":"Nat. Biotechnol."},{"key":"537_CR5","doi-asserted-by":"crossref","unstructured":"Dunn, S. J., Li, M. A., Carbognin, E., Smith, A. & Martello, G. A common molecular logic determines embryonic stem cell self-renewal and reprogramming. EMBO J. 38 (2019).","DOI":"10.15252\/embj.2018100003"},{"key":"537_CR6","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-2798-1","volume":"20","author":"A Bonnaffoux","year":"2019","unstructured":"Bonnaffoux, A. et al. WASABI: a dynamic iterative framework for gene regulatory network inference. BMC Bioinforma. 20, 220 (2019).","journal-title":"BMC Bioinforma."},{"key":"537_CR7","doi-asserted-by":"crossref","unstructured":"Chevalier, S., No\u00ebl, V., Calzone, L., Zinovyev, A. & Paulev\u00e9, L. Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision. In Abate, A., Petrov, T. & Wolf, V. (eds.) Computational Methods in Systems Biology, Lecture Notes in Computer Science, 193\u2013209 (Springer International Publishing, Cham, 2020).","DOI":"10.1007\/978-3-030-60327-4_11"},{"key":"537_CR8","doi-asserted-by":"publisher","unstructured":"Bolteau, M., Chebouba, L., David, L., Bourdon, J. & Guziolowski, C. Boolean Network Models of Human Preimplantation Development. J. Comput. Biol. https:\/\/doi.org\/10.1089\/cmb.2024.0517 (2024).","DOI":"10.1089\/cmb.2024.0517"},{"key":"537_CR9","doi-asserted-by":"publisher","first-page":"1625\u20131640.e6","DOI":"10.1016\/j.stem.2021.04.027","volume":"28","author":"D Meistermann","year":"2021","unstructured":"Meistermann, D. et al. Integrated pseudotime analysis of human pre-implantation embryo single-cell transcriptomes reveals the dynamics of lineage specification. Cell Stem Cell 28, 1625\u20131640.e6 (2021).","journal-title":"Cell Stem Cell"},{"key":"537_CR10","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s12859-018-2034-4","volume":"19","author":"L Chebouba","year":"2018","unstructured":"Chebouba, L., Miannay, B., Boughaci, D. & Guziolowski, C. Discriminate the response of acute myeloid leukemia patients to treatment by using proteomics data and answer set programming. BMC Bioinforma. 19, 15\u201326 (2018).","journal-title":"BMC Bioinforma."},{"key":"537_CR11","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1093\/bioinformatics\/btw738","volume":"33","author":"S Videla","year":"2017","unstructured":"Videla, S., Saez-Rodriguez, J., Guziolowski, C., Siegel, A. & Wren, J. caspo: a toolbox for automated reasoning on the response of logical signaling networks families. Bioinformatics 33, 947\u2013950 (2017).","journal-title":"Bioinformatics"},{"key":"537_CR12","doi-asserted-by":"crossref","unstructured":"Bolteau, M., Bourdon, J., David, L. & Guziolowski, C. Inferring Boolean Networks from Single-Cell Human Embryo Datasets. In Guo, X., Mangul, S., Patterson, M. & Zelikovsky, A. (eds.) Bioinformatics Research and Applications, Lecture Notes in Computer Science, 431\u2013441 (Springer Nature, Singapore, 2023).","DOI":"10.1007\/978-981-99-7074-2_34"},{"key":"537_CR13","doi-asserted-by":"publisher","first-page":"1627","DOI":"10.1101\/gr.276665.122","volume":"32","author":"C Zhao","year":"2022","unstructured":"Zhao, C. et al. Single-cell multi-omics of human preimplantation embryos shows susceptibility to glucocorticoids. Genome Res. 32, 1627\u20131641 (2022).","journal-title":"Genome Res."},{"key":"537_CR14","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1016\/j.cell.2016.03.023","volume":"165","author":"S Petropoulos","year":"2016","unstructured":"Petropoulos, S. et al. Single-cell rna-seq reveals lineage and x chromosome dynamics in human preimplantation embryos. Cell 165, 1012\u20131026 (2016).","journal-title":"Cell"},{"key":"537_CR15","doi-asserted-by":"crossref","unstructured":"Lefebvre, M., Gaignard, A., Folschette, M., Bourdon, J. & Guziolowski, C. Large-scale regulatory and signaling network assembly through linked open data. Database 2021 (2021).","DOI":"10.1093\/database\/baaa113"},{"key":"537_CR16","first-page":"D489","volume":"48","author":"I Rodchenkov","year":"2019","unstructured":"Rodchenkov, I. et al. Pathway Commons 2019 Update: integration, analysis and exploration of pathway data. 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