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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Cell Specific Gene Expression01:58

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Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

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The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
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Overview of Cell Signaling01:23

Overview of Cell Signaling

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Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
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Cell Signaling Feedback Loops01:07

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Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
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Upon receiving an input signal, the cellular response rapidly increases until a threshold is reached. Beyond this threshold, a negative feedback loop...
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Chromatin Modification in iPS Cells01:32

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Chromatin modification alters gene expression; therefore, scientists can add histone-modifying enzymes, histone variants, and chromatin remodeling complexes to somatic cells to aid reprogramming into pluripotent stem (iPS) cells.
Compact chromatin makes reprogramming difficult. Enzymes, such as histone demethylases and acetyltransferases, are often added during reprogramming to loosen the chromatin, making the DNA more accessible to transcription factors. Molecules that inhibit histone...
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Video Experimental Relacionado

Updated: Jan 13, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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Decodificación de las transiciones del estado celular impulsadas por la comunicación dinámica intercelular en

Lulu Yan1, Dongyan Zhang1, Xiaoqiang Sun2,3

  • 1School of Mathematics, Sun Yat-sen University, Guangzhou, China.

Nature computational science
|January 6, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Desarrollamos CCCvelo, un nuevo método para mapear las transiciones de destino celular impulsadas por la comunicación celular. Este enfoque reconstruye la dinámica espaciotemporal, revelando cómo la comunicación orquesta el desarrollo y la enfermedad.

Palabras clave:
transcriptómica espacialbiología computacionalcomunicación intercelulartransiciones del estado celularaprendizaje automáticomodelado de sistemasgenómicabiología del desarrollobiología del cáncer

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Área de la Ciencia:

  • Biología Computacional
  • Genómica
  • Biología del Desarrollo

Sus antecedentes:

  • La determinación del destino celular depende de complejas señales intracelulares e intercelulares.
  • La transcriptómica espacial (ST) ofrece información sobre estos procesos, pero el modelado de las transiciones del estado celular (CST) impulsadas por la comunicación intercelular (CCC) es un desafío.

Objetivo del estudio:

  • Presentar CCCvelo, un marco computacional novedoso para reconstruir la dinámica de CST impulsada por CCC.
  • Integrar gradientes de señalización intercelular y cascadas de factores de transcripción intracelular para modelar la dinámica de la expresión génica.

Principales métodos:

  • Desarrolló CCCvelo, un modelo cinético no lineal multiescala unificado.
  • Diseñó PINN-CELL, un algoritmo de aprendizaje de coevolución de redes neuronales informadas por la física para la optimización de parámetros y el ordenamiento pseudotemporal.
  • Aplicó CCCvelo a conjuntos de datos de ST de alta resolución del córtex de ratón, desarrollo embrionario y cáncer de próstata humano.

Principales resultados:

  • CCCvelo reconstruyó con éxito trayectorias morfogenéticas conocidas.
  • El método descubrió la reprogramación de la señalización dinámica de CCC durante la progresión de CST.
  • Demostró la capacidad de inferir la dinámica espaciotemporal de las transiciones del estado celular gobernadas por CCC.

Conclusiones:

  • CCCvelo proporciona una herramienta poderosa para comprender la determinación del destino celular impulsada por CCC.
  • El marco avanza el análisis de datos de transcriptómica espacial para estudios de desarrollo y enfermedades.
  • Destaca la importancia de la reprogramación dinámica de la red de señalización en la orquestación de las transiciones del estado celular.