Search research articles

ACERCA DE JoVE

Visión General Liderazgo Blog Centro de Ayuda JoVE

AUTORES

Proceso de Publicación Consejo Editorial Alcance y Políticas Revisión por Pares Preguntas Frecuentes Enviar

BIBLIOTECARIOS

Testimonios Suscripciones Acceso Recursos Consejo Asesor de Bibliotecas Preguntas Frecuentes

INVESTIGACIÓN

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archivo

EDUCACIÓN

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Centro de Recursos para Profesores Sitio de Profesores

Términos y Condiciones de Uso

Política de Privacidad

Videos de Conceptos Relacionados

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...

Cooperative Allosteric Transitions

Cooperative Allosteric Transitions

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por

Same author

Mapping and engineering the human cell-cell interactome.

Nature biotechnology·2026

Same author

Programmable pathway profiles reveal signaling principles of TGF-β superfamily receptors.

bioRxiv : the preprint server for biology·2026

Same author

A centrin-Sfi1 myoneme fishnet powers ultrafast calcium-triggered contraction in the giant ciliate <i>Spirostomum ambiguum</i>.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Genome-wide chromatin recording resolves dynamic cell state changes.

bioRxiv : the preprint server for biology·2026

Same author

Minimizing co-growth as a broad predictor of community robustness.

bioRxiv : the preprint server for biology·2026

Same author

Engineering multiple levels of specificity in an RNA viral vector.

Nature communications·2026

Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026

Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026

Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026

Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026

Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026

Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026

Ver todos los artículos relacionados

Search research articles

Video Experimental Relacionado

Updated: Jan 13, 2026

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

Cálculo entrenable en redes moleculares

Kristina Trifonova¹, Martin J Falk¹, Mason Rouches¹

¹James Franck Institute, University of Chicago, Chicago, IL 60637.

bioRxiv : the preprint server for biology

|January 8, 2026

Resumen

Este resumen es generado por máquina.

Este estudio presenta un mecanismo molecular para el aprendizaje celular no genético, que permite a las células adaptarse y entrenarse para diversas tareas sin alteración genética. Propone un nuevo marco para el diseño de circuitos celulares sintéticos entrenables.

Palabras clave:

aprendizaje celular no genético redes moleculares biología sintética sistemas de entrenamiento molecular circuitos celulares sintéticos

Más Videos Relacionados

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Videos de Experimentos Relacionados

Last Updated: Jan 13, 2026

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

DNA-Tethered RNA Polymerase for Programmable In vitro Transcription and Molecular Computation

Published on: December 29, 2021

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Área de la Ciencia:

Biología de Sistemas Moleculares; Biología Sintética; Neurociencia Computacional

Sus antecedentes:

El aprendizaje no genético en células individuales carece de un mecanismo molecular definido.
Los modelos existentes para el entrenamiento celular son limitados en comparación con el aprendizaje de circuitos neuronales.

Objetivo del estudio:

Identificar un mecanismo molecular mínimo para el aprendizaje celular no genético.
Desarrollar una regla de entrenamiento molecular general aplicable a diversas tareas celulares.
Informar el diseño de circuitos celulares sintéticos entrenables.

Principales métodos:

Se utilizaron principios de redes neuronales de Boltzmann.
Se modelaron redes de interacción densas y reversibles con especies mediadoras.
Se implementó un esquema autorregulatorio sensible a la tasa para el entrenamiento.

Principales resultados:

Se demostró un mecanismo molecular para el aprendizaje no genético en células.
Se exhibió una regla de entrenamiento similar a la de Hebbian adaptable a diversas tareas (p. ej., condicionamiento pavloviano, clasificación).
Se estableció que la regla de entrenamiento es independiente del modelo y aplicable a redes complejas.

Conclusiones:

Se propuso un mecanismo molecular general para el aprendizaje y la adaptación celular.
Se destacó el potencial de los sistemas moleculares para aprender estadísticas ambientales.
Se sugirieron principios de diseño para crear circuitos celulares sintéticos entrenables.