[{"fulldoi":"https://doi.org/10.1103/mbjk-v4ym","publisher":"American Physical Society","dataavailabilitystatement":"Code to evaluate PI, to run algorithmic implementations of ALP and RLP decoding, and to\r\nperform simulations is publicly available at https://github.com/alex-chenyi-zhang/nonlocdec_pici.","oa_version":"Published Version","day":"15","das_tickbox":"1","volume":137,"oa":1,"doi":"10.1103/mbjk-v4ym","language":[{"iso":"eng"}],"article_number":"038401","abstract":[{"lang":"eng","text":"In many developmental systems, cells differentiate into a tissue by reading out morphogen concentration fields, a process fundamentally limited by noise. How much can the precision of this process be improved by nonlocal information, e.g., via cell-cell communication? Using a Bayes-optimal framework, we show that positional inference depends crucially on morphogen spatial correlations and on the \"structural prior\" that encodes the geometry of the cellular lattice performing the readout, thereby determining what a cell can reliably assume about the position of its neighbors when interpreting nonlocal morphogen signals. We derive upper bounds on positional information gain due to nonlocal readout and identify signal processing algorithms that approximate optimal positional inference, as well as simple chemical reaction schemes which implement such algorithms. Our theory suggests that correlational information can be exploited to significantly enhance developmental precision."}],"OA_place":"publisher","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"_id":"22326","author":[{"last_name":"Zhang","id":"81b43fb8-c9d5-11ef-bf68-ade532a1f204","full_name":"Zhang, Chen Y","first_name":"Chen Y"},{"full_name":"Mateu Hoyos, Pablo","id":"50b236c7-50c1-11ef-bb9a-a2375694f8b5","first_name":"Pablo","last_name":"Mateu Hoyos"},{"last_name":"Brückner","full_name":"Brückner, David","id":"e1e86031-6537-11eb-953a-f7ab92be508d","orcid":"0000-0001-7205-2975","first_name":"David"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper","last_name":"Tkačik"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2026-07-16T09:58:04Z","supplementarymaterial":"no","intvolume":"       137","has_accepted_license":"1","OA_type":"hybrid","quality_controlled":"1","project":[{"_id":"7bfe6a29-9f16-11ee-852c-c0da5e2045d9","name":"Transcription in 4D: the dynamic interplay between chromatin architecture and gene expression in developing pseudo-embryos","grant_number":"101118866"}],"publication":"Physical Review Letters","scopus_import":"1","type":"journal_article","PlanS_conform":"1","researchdata_availability":"no","status":"public","citation":{"ista":"Zhang CY, Mateu Hoyos P, Brückner D, Tkačik G. 2026. Nonlocal decoding of positional and correlational information during development. Physical Review Letters. 137, 038401.","mla":"Zhang, Chen Y., et al. “Nonlocal Decoding of Positional and Correlational Information during Development.” <i>Physical Review Letters</i>, vol. 137, 038401, American Physical Society, 2026, doi:<a href=\"https://doi.org/10.1103/mbjk-v4ym\">10.1103/mbjk-v4ym</a>.","ieee":"C. Y. Zhang, P. Mateu Hoyos, D. Brückner, and G. Tkačik, “Nonlocal decoding of positional and correlational information during development,” <i>Physical Review Letters</i>, vol. 137. American Physical Society, 2026.","ama":"Zhang CY, Mateu Hoyos P, Brückner D, Tkačik G. Nonlocal decoding of positional and correlational information during development. <i>Physical Review Letters</i>. 2026;137. doi:<a href=\"https://doi.org/10.1103/mbjk-v4ym\">10.1103/mbjk-v4ym</a>","apa":"Zhang, C. Y., Mateu Hoyos, P., Brückner, D., &#38; Tkačik, G. (2026). Nonlocal decoding of positional and correlational information during development. <i>Physical Review Letters</i>. American Physical Society. <a href=\"https://doi.org/10.1103/mbjk-v4ym\">https://doi.org/10.1103/mbjk-v4ym</a>","short":"C.Y. Zhang, P. Mateu Hoyos, D. Brückner, G. Tkačik, Physical Review Letters 137 (2026).","chicago":"Zhang, Chen Y, Pablo Mateu Hoyos, David Brückner, and Gašper Tkačik. “Nonlocal Decoding of Positional and Correlational Information during Development.” <i>Physical Review Letters</i>. American Physical Society, 2026. <a href=\"https://doi.org/10.1103/mbjk-v4ym\">https://doi.org/10.1103/mbjk-v4ym</a>."},"month":"07","date_published":"2026-07-15T00:00:00Z","department":[{"_id":"GaTk"},{"_id":"EdHa"},{"_id":"GradSch"}],"acknowledgement":"This work was supported in part\r\nby European Research Council No. ERC-2023-SyG\r\n“DynaTrans” Grant No. 101118866 (G. T.). We thank\r\nPieter Rein ten Wolde and Vahe Galstyan for stimulating\r\ndiscussions.","ddc":["530"],"title":"Nonlocal decoding of positional and correlational information during development","file_date_updated":"2026-07-16T09:54:55Z","article_type":"original","corr_author":"1","year":"2026","article_processing_charge":"Yes (via OA deal)","date_created":"2026-07-14T05:38:28Z","file":[{"checksum":"28861d31d0f6cf541aaca04faaed1767","file_id":"22352","success":1,"creator":"dernst","file_size":2550345,"access_level":"open_access","date_created":"2026-07-16T09:54:55Z","content_type":"application/pdf","date_updated":"2026-07-16T09:54:55Z","file_name":"2026_PhysicalReviewLetters_Zhang.pdf","relation":"main_file"}],"publication_identifier":{"eissn":[" 1079-7114"],"issn":["0031-9007"]},"publication_status":"published"},{"date_updated":"2026-02-18T08:01:00Z","arxiv":1,"_id":"21269","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","image":"/images/cc_by.png"},"author":[{"last_name":"Zhang","id":"81b43fb8-c9d5-11ef-bf68-ade532a1f204","full_name":"Zhang, Chen Y","first_name":"Chen Y"},{"last_name":"Rosa","first_name":"Angelo","full_name":"Rosa, Angelo"},{"last_name":"Sanguinetti","first_name":"Guido","full_name":"Sanguinetti, Guido"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"043006","language":[{"iso":"eng"}],"abstract":[{"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.","lang":"eng"}],"OA_place":"publisher","doi":"10.1103/gy1p-4256","oa":1,"external_id":{"arxiv":["2409.14425"]},"volume":3,"day":"21","oa_version":"Published Version","publisher":"American Physical Society","fulldoi":"https://doi.org/10.1103/gy1p-4256","date_created":"2026-02-17T07:53:01Z","publication_identifier":{"issn":["2835-8279"]},"file":[{"file_size":1888053,"relation":"main_file","file_name":"2025_PRXLife_Zhang.pdf","date_created":"2026-02-18T07:57:39Z","access_level":"open_access","content_type":"application/pdf","date_updated":"2026-02-18T07:57:39Z","checksum":"76ddfee3efdb4c9d085059b5a142ed78","creator":"dernst","success":1,"file_id":"21314"}],"publication_status":"published","article_type":"original","corr_author":"1","file_date_updated":"2026-02-18T07:57:39Z","year":"2025","article_processing_charge":"Yes","date_published":"2025-10-21T00:00:00Z","department":[{"_id":"GaTk"}],"DOAJ_listed":"1","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.","ddc":["570"],"title":"bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction","month":"10","status":"public","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.","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>.","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.","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>","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>","short":"C.Y. Zhang, A. Rosa, G. Sanguinetti, PRX Life 3 (2025).","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>."},"type":"journal_article","PlanS_conform":"1","quality_controlled":"1","publication":"PRX Life","issue":"4","intvolume":"         3","has_accepted_license":"1","OA_type":"gold"}]