@article{22326,
  abstract     = {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.},
  author       = {Zhang, Chen Y and Mateu Hoyos, Pablo and Brückner, David and Tkačik, Gašper},
  issn         = { 1079-7114},
  journal      = {Physical Review Letters},
  publisher    = {American Physical Society},
  title        = {{Nonlocal decoding of positional and correlational information during development}},
  doi          = {10.1103/mbjk-v4ym},
  volume       = {137},
  year         = {2026},
}

@article{21269,
  abstract     = {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.},
  author       = {Zhang, Chen Y and Rosa, Angelo and Sanguinetti, Guido},
  issn         = {2835-8279},
  journal      = {PRX Life},
  number       = {4},
  publisher    = {American Physical Society},
  title        = {{bioSBM: A random graph model to integrate epigenomic data in chromatin structure prediction}},
  doi          = {10.1103/gy1p-4256},
  volume       = {3},
  year         = {2025},
}