Investigating Histone Modification Dynamics by Mechanistic Computational Modeling.
Methods Mol Biol
; 2529: 441-473, 2022.
Article
en En
| MEDLINE
| ID: mdl-35733026
ABSTRACT
The maintenance of transcriptional states regulated by histone modifications and controlled switching between these states are fundamental concepts in our understanding of nucleosome-mediated epigenetic memory. Any approach relying on genome-wide bioinformatic analyses alone offers limited scope for dissecting the molecular mechanisms involved in maintenance and switching. Mechanistic mathematical models-describing the dynamics of histone modifications at individual genomic loci-offer an alternative way to investigate these mechanisms. These models, in conjunction with quantitative experimental data-ChIP data, quantification of mRNA levels, and single-cell fluorescence tracking in clonal lineages-can generate predictions that drive more targeted experiments, allowing us to understand mechanisms that would be challenging to unravel by a purely experimental approach. In this chapter, we describe a generic stochastic modeling framework that can be used to capture histone modification dynamics and associated molecular processes-including transcription and read-write feedback by chromatin modifying complexes-at individual genomic loci. Using a specific example-transcriptional silencing by Polycomb-mediated H3K27 methylation-we demonstrate how to construct and simulate a stochastic histone modification model. We provide a step-by-step guide to programming simulations for such a model and discuss how to analyze the simulation output.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Histonas
/
Código de Histonas
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Methods Mol Biol
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2022
Tipo del documento:
Article
País de afiliación:
Reino Unido