Time-Based Systems Biology Approaches to Capture and Model Dynamic Gene Regulatory Networks.
Annu Rev Plant Biol
; 72: 105-131, 2021 06 17.
Article
em En
| MEDLINE
| ID: mdl-33667112
All aspects of transcription and its regulation involve dynamic events. However, capturing these dynamic events in gene regulatory networks (GRNs) offers both a promise and a challenge. The promise is that capturing and modeling the dynamic changes in GRNs will allow us to understand how organisms adapt to a changing environment. The ability to mount a rapid transcriptional response to environmental changes is especially important in nonmotile organisms such as plants. The challenge is to capture these dynamic, genome-wide events and model them in GRNs. In this review, we cover recent progress in capturing dynamic interactions of transcription factors with their targets-at both the local and genome-wide levels-and how they are used to learn how GRNs operate as a function of time. We also discuss recent advances that employ time-based machine learning approaches to forecast gene expression at future time points, a key goal of systems biology.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Biologia de Sistemas
/
Redes Reguladoras de Genes
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
Annu Rev Plant Biol
Assunto da revista:
BOTANICA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Chile
País de publicação:
Estados Unidos