Vercellone, Francesca (2025) Polymer physics of single-cell chromatin architecture: structural and epigenetic variability. [Tesi di dottorato]

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Tipologia del documento: Tesi di dottorato
Lingua: English
Titolo: Polymer physics of single-cell chromatin architecture: structural and epigenetic variability
Autori:
Autore
Email
Vercellone, Francesca
francesca.vercellone@unina.it
Data: 9 Dicembre 2025
Numero di pagine: 110
Istituzione: Università degli Studi di Napoli Federico II
Dottorato: Computational and quantitative biology
Ciclo di dottorato: 38
Coordinatore del Corso di dottorato:
nome
email
Ceccarelli, Michele
michele.ceccarelli@unina.it
Tutor:
nome
email
Nicodemi, Mario
[non definito]
Data: 9 Dicembre 2025
Numero di pagine: 110
Parole chiave: chromatin modeling, epigenetics, polymer physics, 3D genomics
Settori scientifico-disciplinari del MIUR: Area 05 - Scienze biologiche > BIO/11 - Biologia molecolare
Area 02 - Scienze fisiche > FIS/02 - Fisica teorica, modelli e metodi matematici
Informazioni aggiuntive: TRENTOTTESIMO (38) CICLO
Depositato il: 07 Apr 2026 06:32
Ultima modifica: 10 Ago 2026 14:13
URI: https://www.fedoa.unina.it/id/eprint/17079

Abstract

The three-dimensional (3D) genome organization plays a fundamental role in regulating gene expression and cellular identity. Advances in chromosome conformation capture technologies, such as Hi-C, have revealed the multiscale architecture of chromatin. However, population-averaged Hi-C data obscure the structural variability that exists across individual cells. Emerging single-cell Hi-C (scHi-C) methods now provide direct access to this variability, but their interpretation remains challenging due to the extreme data sparsity. In this thesis, I apply a polymer physics-based framework to infer 3D chromatin architecture from sparse single-cell Hi-C data, bridging the gap between statistical and mechanistic models of genome organization. Using polymer physics models and computational approaches, I reconstruct 3D structures from both bulk and single-cell Hi-C datasets. When applied to experimental scHi-C data, this framework reveals reproducible folding patterns and enables accurate identification of single-cell structural features. Integrating polymer modeling with epigenomic information further uncovers distinct classes of chromatin domains associated with functionally different states, revealing a connection between structural and epigenetic variability. Overall, this work demonstrates that polymer physics provides a quantitative and interpretable framework to explore 3D genome architecture at single-cell resolution, paving the way for future integration with multi-omics and machine-learning approaches to unravel the physical basis of cellular diversity.

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