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Sensibilidad de parámetros y anticipación de transiciones críticas en la dinámica bistable de toxina-antitoxina
Shankha Narayan Chattopadhyay1, Inayat Ullah Irshad2, Ajeet K Sharma3
1Department of Mathematics, Indian Institute of Technology Ropar, Rupnagar, 140001, India.
Computers in biology and medicine
|February 8, 2026
Resumen
El modelado matemático revela parámetros clave en los sistemas bacterianos de toxina-antitoxina que impulsan la formación de células persistentes. Los indicadores estadísticos pueden predecir de manera confiable estas transiciones críticas, ayudando a comprender la tolerancia bacteriana a los medicamentos y la recaída de infecciones.
Área de la Ciencia:
- Fisiología Bacteriana
- Biología de Sistemas
- Biología Matemática
Sus antecedentes:
- Los sistemas de toxina-antitoxina son cruciales para la persistencia bacteriana, la tolerancia a los medicamentos y la recaída de infecciones.
- Comprender la regulación de estos sistemas es esencial para desarrollar nuevas estrategias terapéuticas.
Objetivo del estudio:
- Investigar la formación de células bacterianas persistentes mediante modelado matemático.
- Evaluar la predecibilidad de la aparición de células persistentes utilizando medidas estadísticas.
Principales métodos:
- Un modelo matemático que simula interacciones bioquímicas dentro de los operones de toxina-antitoxina.
- Análisis de sensibilidad global para identificar parámetros regulatorios clave.
- Ecuación maestra química y simulaciones de Gillespie para incorporar el ruido intrínseco.
- Indicadores estadísticos multivariantes aplicados en marcos de ventana móvil y expansiva.
Principales resultados:
- Seis parámetros clave influyen significativamente en la transcripción, traducción y recambio de antitoxina bacteriana.
- Un parámetro que controla la represión de la antitoxina impulsa la histéresis entre estados fisiológicos.
- Se detectaron transiciones críticas a células persistentes utilizando doce indicadores estadísticos.
- El análisis de ventana expansiva identificó diferentes tipos de advertencias predictivas (parpadeo, sostenidas, espurias).
Conclusiones:
- Las medidas estadísticas multivariantes pueden predecir de manera confiable las transiciones críticas en sistemas bacterianos.
- Este marco ayuda a comprender la pérdida de resiliencia y a desarrollar estrategias contra infecciones persistentes.
Palabras clave:
BiestabilidadTransiciones críticasSimulaciones de GillespieAnálisis de sensibilidad globalIndicadores multivariantesMás Videos Relacionados
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