Computational Systems Biology of Morphogenesis.
Methods Mol Biol
; 2399: 343-365, 2022.
Article
en En
| MEDLINE
| ID: mdl-35604563
ABSTRACT
Extracting mechanistic knowledge from the spatial and temporal phenotypes of morphogenesis is a current challenge due to the complexity of biological regulation and their feedback loops. Furthermore, these regulatory interactions are also linked to the biophysical forces that shape a developing tissue, creating complex interactions responsible for emergent patterns and forms. Here we show how a computational systems biology approach can aid in the understanding of morphogenesis from a mechanistic perspective. This methodology integrates the modeling of tissues and whole-embryos with dynamical systems, the reverse engineering of parameters or even whole equations with machine learning, and the generation of precise computational predictions that can be tested at the bench. To implement and perform the computational steps in the methodology, we present user-friendly tools, computer code, and guidelines. The principles of this methodology are general and can be adapted to other model organisms to extract mechanistic knowledge of their morphogenesis.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Biología Computacional
/
Biología de Sistemas
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:
Estados Unidos