---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '22326'
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.
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."
article_number: '038401'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Chen Y
  full_name: Zhang, Chen Y
  id: 81b43fb8-c9d5-11ef-bf68-ade532a1f204
  last_name: Zhang
- first_name: Pablo
  full_name: Mateu Hoyos, Pablo
  id: 50b236c7-50c1-11ef-bb9a-a2375694f8b5
  last_name: Mateu Hoyos
- first_name: David
  full_name: Brückner, David
  id: e1e86031-6537-11eb-953a-f7ab92be508d
  last_name: Brückner
  orcid: 0000-0001-7205-2975
- first_name: Gašper
  full_name: Tkačik, Gašper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkačik
  orcid: 0000-0002-6699-1455
citation:
  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>
  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>.
  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.
  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>.
  short: C.Y. Zhang, P. Mateu Hoyos, D. Brückner, G. Tkačik, Physical Review Letters
    137 (2026).
corr_author: '1'
das_tickbox: '1'
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."
date_created: 2026-07-14T05:38:28Z
date_published: 2026-07-15T00:00:00Z
date_updated: 2026-07-16T09:58:04Z
day: '15'
ddc:
- '530'
department:
- _id: GaTk
- _id: EdHa
- _id: GradSch
doi: 10.1103/mbjk-v4ym
file:
- access_level: open_access
  checksum: 28861d31d0f6cf541aaca04faaed1767
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  creator: dernst
  date_created: 2026-07-16T09:54:55Z
  date_updated: 2026-07-16T09:54:55Z
  file_id: '22352'
  file_name: 2026_PhysicalReviewLetters_Zhang.pdf
  file_size: 2550345
  relation: main_file
  success: 1
file_date_updated: 2026-07-16T09:54:55Z
fulldoi: https://doi.org/10.1103/mbjk-v4ym
has_accepted_license: '1'
intvolume: '       137'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
project:
- _id: 7bfe6a29-9f16-11ee-852c-c0da5e2045d9
  grant_number: '101118866'
  name: 'Transcription in 4D: the dynamic interplay between chromatin architecture
    and gene expression in developing pseudo-embryos'
publication: Physical Review Letters
publication_identifier:
  eissn:
  - ' 1079-7114'
  issn:
  - 0031-9007
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
researchdata_availability: no
scopus_import: '1'
status: public
supplementarymaterial: no
title: Nonlocal decoding of positional and correlational information during development
tmp:
  image: /images/cc_by.png
  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)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 137
year: '2026'
...
---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
PlanS_conform: '1'
_id: '21269'
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.
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.
article_number: '043006'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Chen Y
  full_name: Zhang, Chen Y
  id: 81b43fb8-c9d5-11ef-bf68-ade532a1f204
  last_name: Zhang
- first_name: Angelo
  full_name: Rosa, Angelo
  last_name: Rosa
- first_name: Guido
  full_name: Sanguinetti, Guido
  last_name: Sanguinetti
citation:
  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>'
  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>.'
  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.'
  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.'
  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>.'
  short: C.Y. Zhang, A. Rosa, G. Sanguinetti, PRX Life 3 (2025).
corr_author: '1'
date_created: 2026-02-17T07:53:01Z
date_published: 2025-10-21T00:00:00Z
date_updated: 2026-02-18T08:01:00Z
day: '21'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1103/gy1p-4256
external_id:
  arxiv:
  - '2409.14425'
file:
- access_level: open_access
  checksum: 76ddfee3efdb4c9d085059b5a142ed78
  content_type: application/pdf
  creator: dernst
  date_created: 2026-02-18T07:57:39Z
  date_updated: 2026-02-18T07:57:39Z
  file_id: '21314'
  file_name: 2025_PRXLife_Zhang.pdf
  file_size: 1888053
  relation: main_file
  success: 1
file_date_updated: 2026-02-18T07:57:39Z
fulldoi: https://doi.org/10.1103/gy1p-4256
has_accepted_license: '1'
intvolume: '         3'
issue: '4'
language:
- iso: eng
month: '10'
oa: 1
oa_version: Published Version
publication: PRX Life
publication_identifier:
  issn:
  - 2835-8279
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
status: public
title: 'bioSBM: A random graph model to integrate epigenomic data in chromatin structure
  prediction'
tmp:
  image: /images/cc_by.png
  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)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 3
year: '2025'
...