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Análisis y simulaciones de modelos booleanos reproducibles con el conjunto de software CoLoMoTo: un tutorial

Vincent Noël1,2,3, Aurélien Naldi4, Laurence Calzone1,2,3

  • 1Institut Curie, Université PSL, 75005 Paris, France.

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|August 27, 2025
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Este resumen es generado por máquina.

Este tutorial simplifica el análisis de redes celulares complejas utilizando la suite CoLoMoTo y Python. Permite análisis dinámicos reproducibles de redes biológicas, ayudando a los investigadores a comprender los procesos celulares.

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Área de la Ciencia:

  • Biología computacional
  • Biología de sistemas
  • La bioinformática

Sus antecedentes:

  • Los modelos lógicos son cruciales para comprender las redes celulares complejas.
  • La integración de diversas herramientas computacionales puede ser un reto para los investigadores.
  • El análisis reproducible es esencial para validar los modelos de redes biológicas.

Objetivo del estudio:

  • Para proporcionar un tutorial para la instalación y uso de más de 20 herramientas dentro de la suite de software CoLoMoTo.
  • Permitir análisis dinámicos sofisticados y reproducibles de modelos lógicos de redes celulares utilizando Python.
  • Servir como modelo para el análisis de varios modelos de redes biológicas.

Principales métodos:

  • Instalación e integración de más de 20 herramientas computacionales en el conjunto de software CoLoMoTo.
  • Utilizando Python para análisis dinámicos accesibles y reproducibles.
  • Aplicación de herramientas específicas como GINsim, bioLQM, BNS y MaBoSS para la visualización de redes, análisis de atractores, extracción de módulos y simulaciones.
  • Aprovechando los portátiles Jupyter para flujos de trabajo de análisis integrados y reproducibles.

Principales resultados:

  • Demostración de las instrucciones paso a paso para la instalación e integración de herramientas.
  • Reproducción exitosa y extensión de los resultados de un modelo de proliferación celular de mamíferos publicado anteriormente.
  • Análisis completo que incluye visualización de la red, análisis del atractor, extracción de módulos y simulaciones estocásticas.
  • Creación de una plantilla de análisis reproducible con Jupyter Notebooks.

Conclusiones:

  • El conjunto CoLoMoTo, integrado con Python, mejora significativamente la reproducibilidad y la sofisticación de los análisis dinámicos para modelos de redes biológicas.
  • El tutorial proporcionado y el Cuaderno Jupyter sirven como recursos valiosos para los investigadores, facilitando los estudios de biología de sistemas complejos.
  • Este enfoque permite a los investigadores realizar análisis en profundidad y explorar extensiones de modelos de redes biológicas de manera eficiente.