arXiv subjects

Alex Chen Yi Zhang

Publications and source records attributed to Alex Chen Yi Zhang.

3 recordsLinked to original sources

arXiv2025

Nonlocal decoding of positional and correlational information during development

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. 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.

physics.bio-ph↗

arXiv2025

bioSBM: a random graph model to integrate epigenomic data in chromatin structure prediction

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 7 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.

q-bio.QM↗

arXiv2023

Bottom-up data integration in polymer models of chromatin organisation

Cellular functions crucially depend on the precise execution of complex biochemical reactions taking place on the chromatin fiber in the tightly packed environment of the cell nucleus. Despite the availability of large data sets probing this process from multiple angles, we still lack a bottom-up framework which can incorporate the sequence-specific nature of biochemistry in a unified model of 3D chromatin dynamics. Here we propose SEMPER (Sequence Enhanced Magnetic PolymER), a novel stochastic polymer model which naturally incorporates observational data about sequence-driven biochemical processes, such as binding of transcription factor proteins, in a 3D model of chromatin structure. By introducing a new algorithm for approximate Bayesian inference, we discuss how to estimate in a robust manner the relative importance of biochemical vs. polymer signals in the determination of the chromatin epigenetic states which is leading to a significant revision of the interpretation of previous models. Furthermore we show that, without additional input from the genome 3D structure, our model can predict with reasonable accuracy some notable and non trivial conformational features of chromatin folding within the nucleus. Our work highlights the importance of introducing physically realistic statistical models for predicting chromatin states from epigenetic data, and opens the way to a new class of more systematic approaches to interpret epigenomic data.

q-bio.QM↗