Your browser doesn't support javascript.
loading
Learning Morphological, Spatial, and Dynamic Models of Cellular Components.
Sun, Huangqingbo; Murphy, Robert F.
Afiliação
  • Sun H; Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Murphy RF; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA. murphy@cmu.edu.
Methods Mol Biol ; 2800: 231-244, 2024.
Article em En | MEDLINE | ID: mdl-38709488
ABSTRACT
In this chapter, we describe protocols for using the CellOrganizer software on the Jupyter Notebook platform to analyze and model cell and organelle shape and spatial arrangement. CellOrganizer is an open-source system for using microscope images to learn statistical models of the structure of cell components and how those components are organized relative to each other. Such models capture the statistical variation in the organization of cellular components by jointly modeling the distributions of their number, shape, and spatial distributions. These models can be created for different cell types or conditions and compared to reflect differences in their spatial organizations. The models are also generative, in that they can be used to synthesize new cell instances reflecting what a model learned and to provide well-structured cell geometries that can be used for biochemical simulations.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software Limite: Humans Idioma: En Revista: Methods Mol Biol Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software Limite: Humans Idioma: En Revista: Methods Mol Biol Ano de publicação: 2024 Tipo de documento: Article