[{"year":"2025","article_number":"043006","publisher":"American Physical Society","ddc":["570"],"article_processing_charge":"Yes","date_published":"2025-10-21T00:00:00Z","volume":3,"intvolume":"         3","author":[{"id":"81b43fb8-c9d5-11ef-bf68-ade532a1f204","full_name":"Zhang, Chen Y","last_name":"Zhang","first_name":"Chen Y"},{"first_name":"Angelo","full_name":"Rosa, Angelo","last_name":"Rosa"},{"last_name":"Sanguinetti","full_name":"Sanguinetti, Guido","first_name":"Guido"}],"issue":"4","_id":"21269","external_id":{"arxiv":["2409.14425"]},"acknowledgement":"G.S. acknowledges co-funding from Next Generation EU, in the context of the National Recovery and Resilience Plan, Investment PE1 - Project FAIR “Future Artificial Intelligence Research”. This resource was co-financed by the Next Generation EU [DM 1555 del 11.10.22]. A.R. acknowledges financial support from PNRR Grant CN 00000013 CN-HPC, M4C2I1.4, spoke 7, funded by Next Generation EU.","status":"public","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"citation":{"ieee":"C. Y. Zhang, A. Rosa, and G. Sanguinetti, “bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction,” <i>PRX Life</i>, vol. 3, no. 4. American Physical Society, 2025.","ama":"Zhang CY, Rosa A, Sanguinetti G. bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction. <i>PRX Life</i>. 2025;3(4). doi:<a href=\"https://doi.org/10.1103/gy1p-4256\">10.1103/gy1p-4256</a>","apa":"Zhang, C. Y., Rosa, A., &#38; Sanguinetti, G. (2025). bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction. <i>PRX Life</i>. American Physical Society. <a href=\"https://doi.org/10.1103/gy1p-4256\">https://doi.org/10.1103/gy1p-4256</a>","mla":"Zhang, Chen Y., et al. “BioSBM: A Random Graph Model to Integrate Epigenomic Data in Chromatin Structure Prediction.” <i>PRX Life</i>, vol. 3, no. 4, 043006, American Physical Society, 2025, doi:<a href=\"https://doi.org/10.1103/gy1p-4256\">10.1103/gy1p-4256</a>.","chicago":"Zhang, Chen Y, Angelo Rosa, and Guido Sanguinetti. “BioSBM: A Random Graph Model to Integrate Epigenomic Data in Chromatin Structure Prediction.” <i>PRX Life</i>. American Physical Society, 2025. <a href=\"https://doi.org/10.1103/gy1p-4256\">https://doi.org/10.1103/gy1p-4256</a>.","short":"C.Y. Zhang, A. Rosa, G. Sanguinetti, PRX Life 3 (2025).","ista":"Zhang CY, Rosa A, Sanguinetti G. 2025. bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction. PRX Life. 3(4), 043006."},"file":[{"success":1,"access_level":"open_access","relation":"main_file","checksum":"76ddfee3efdb4c9d085059b5a142ed78","content_type":"application/pdf","file_name":"2025_PRXLife_Zhang.pdf","file_id":"21314","date_created":"2026-02-18T07:57:39Z","file_size":1888053,"creator":"dernst","date_updated":"2026-02-18T07:57:39Z"}],"arxiv":1,"language":[{"iso":"eng"}],"quality_controlled":"1","oa_version":"Published Version","corr_author":"1","month":"10","department":[{"_id":"GaTk"}],"publication_status":"published","day":"21","doi":"10.1103/gy1p-4256","file_date_updated":"2026-02-18T07:57:39Z","PlanS_conform":"1","DOAJ_listed":"1","type":"journal_article","date_updated":"2026-02-18T08:01:00Z","abstract":[{"lang":"eng","text":"The spatial organization of chromatin within the nucleus plays a crucial role in gene expression and genome function. However, the quantitative relationship between this organization and nuclear biochemical processes remains under debate. In this study, we present a graph-based generative model, bioSBM, designed to capture long-range chromatin interaction patterns from Hi-C data and, importantly, simultaneously link these patterns to biochemical features. Applying bioSBM to Hi-C maps of the GM12878 lymphoblastoid cell line, we identified a latent structure of chromatin interactions, revealing seven distinct communities that strongly align with known biological annotations. Additionally, we infer a linear transformation that maps biochemical observables, such as histone marks, to the parameters of the generative graph model, enabling accurate genome-wide predictions of chromatin contact maps on out-of-sample data, both within the same cell line and on the completely unseen HCT116 cell line under RAD21 depletion. These findings highlight bioSBM's potential as a powerful tool for elucidating the relationship between biochemistry and chromatin architecture and predicting long-range genome organization from independent biochemical data."}],"date_created":"2026-02-17T07:53:01Z","publication":"PRX Life","publication_identifier":{"issn":["2835-8279"]},"oa":1,"has_accepted_license":"1","article_type":"original","OA_place":"publisher","title":"bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","OA_type":"gold"},{"issue":"1","_id":"14277","author":[{"orcid":"0000-0002-1585-2631","first_name":"Daniel R","full_name":"Boocock, Daniel R","id":"453AF628-F248-11E8-B48F-1D18A9856A87","last_name":"Boocock"},{"first_name":"Tsuyoshi","full_name":"Hirashima, Tsuyoshi","last_name":"Hirashima"},{"id":"3A9DB764-F248-11E8-B48F-1D18A9856A87","full_name":"Hannezo, Edouard B","last_name":"Hannezo","first_name":"Edouard B","orcid":"0000-0001-6005-1561"}],"intvolume":"         1","volume":1,"date_published":"2023-07-20T00:00:00Z","article_processing_charge":"Yes","ddc":["570"],"publisher":"American Physical Society","project":[{"grant_number":"851288","name":"Design Principles of Branching Morphogenesis","call_identifier":"H2020","_id":"05943252-7A3F-11EA-A408-12923DDC885E"}],"article_number":"013001","year":"2023","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers","article_type":"original","oa":1,"has_accepted_license":"1","publication_identifier":{"issn":["2835-8279"]},"publication":"PRX Life","date_created":"2023-09-06T08:30:59Z","abstract":[{"text":"Living tissues are characterized by an intrinsically mechanochemical interplay of active physical forces and complex biochemical signaling pathways. Either feature alone can give rise to complex emergent phenomena, for example, mechanically driven glassy dynamics and rigidity transitions, or chemically driven reaction-diffusion instabilities. An important question is how to quantitatively assess the contribution of these different cues to the large-scale dynamics of biological materials. We address this in Madin-Darby canine kidney (MDCK) monolayers, considering both mechanochemical feedback between extracellular signal-regulated kinase (ERK) signaling activity and cellular density as well as a mechanically active tissue rheology via a self-propelled vertex model. We show that the relative strength of active migration forces to mechanochemical couplings controls a transition from a uniform active glass to periodic spatiotemporal waves. We parametrize the model from published experimental data sets on MDCK monolayers and use it to make new predictions on the correlation functions of cellular dynamics and the dynamics of topological defects associated with the oscillatory phase of cells. Interestingly, MDCK monolayers are best described by an intermediary parameter region in which both mechanochemical couplings and noisy active propulsion have a strong influence on the dynamics. Finally, we study how tissue rheology and ERK waves produce feedback on one another and uncover a mechanism via which tissue fluidity can be controlled by mechanochemical waves at both the local and global levels.","lang":"eng"}],"date_updated":"2025-04-14T07:52:27Z","type":"journal_article","file_date_updated":"2023-09-15T06:30:50Z","ec_funded":1,"doi":"10.1103/prxlife.1.013001","day":"20","department":[{"_id":"EdHa"}],"publication_status":"published","corr_author":"1","month":"07","oa_version":"Published Version","quality_controlled":"1","language":[{"iso":"eng"}],"file":[{"success":1,"access_level":"open_access","relation":"main_file","checksum":"f881d98c89eb9f1aa136d7b781511553","content_type":"application/pdf","file_name":"2023_PRXLife_Boocock.pdf","file_id":"14335","date_created":"2023-09-15T06:30:50Z","file_size":2559520,"creator":"dernst","date_updated":"2023-09-15T06:30:50Z"}],"citation":{"apa":"Boocock, D. R., Hirashima, T., &#38; Hannezo, E. B. (2023). Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers. <i>PRX Life</i>. American Physical Society. <a href=\"https://doi.org/10.1103/prxlife.1.013001\">https://doi.org/10.1103/prxlife.1.013001</a>","ama":"Boocock DR, Hirashima T, Hannezo EB. Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers. <i>PRX Life</i>. 2023;1(1). doi:<a href=\"https://doi.org/10.1103/prxlife.1.013001\">10.1103/prxlife.1.013001</a>","ieee":"D. R. Boocock, T. Hirashima, and E. B. Hannezo, “Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers,” <i>PRX Life</i>, vol. 1, no. 1. American Physical Society, 2023.","ista":"Boocock DR, Hirashima T, Hannezo EB. 2023. Interplay between mechanochemical patterning and glassy dynamics in cellular monolayers. PRX Life. 1(1), 013001.","chicago":"Boocock, Daniel R, Tsuyoshi Hirashima, and Edouard B Hannezo. “Interplay between Mechanochemical Patterning and Glassy Dynamics in Cellular Monolayers.” <i>PRX Life</i>. American Physical Society, 2023. <a href=\"https://doi.org/10.1103/prxlife.1.013001\">https://doi.org/10.1103/prxlife.1.013001</a>.","short":"D.R. Boocock, T. Hirashima, E.B. Hannezo, PRX Life 1 (2023).","mla":"Boocock, Daniel R., et al. “Interplay between Mechanochemical Patterning and Glassy Dynamics in Cellular Monolayers.” <i>PRX Life</i>, vol. 1, no. 1, 013001, American Physical Society, 2023, doi:<a href=\"https://doi.org/10.1103/prxlife.1.013001\">10.1103/prxlife.1.013001</a>."},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"status":"public","acknowledgement":"We thank all members of the Hannezo group for discussions and suggestions, as well as Sound Wai Phow for technical assistance. This work received funding from the European Research Council under the EU Horizon 2020 research and innovation program Grant Agreement No. 851288 (E.H.), JSPS KAKENHI Grant No. 21H05290, and the Ministry of Education under the Research Centres of Excellence program through the MBI at NUS."}]